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Guan, Qingji, Qinrun Chen, and Yaping Huang. "An Improved Heteroscedastic Modeling Method for Chest X-ray Image Classification with Noisy Labels." Algorithms 16, no. 5 (2023): 239. http://dx.doi.org/10.3390/a16050239.
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Chest X-ray image classification suffers from the high inter-similarity in appearance that is vulnerable to noisy labels. The data-dependent and heteroscedastic characteristic label noise make chest X-ray image classification more challenging. To address this problem, in this paper, we first revisit the heteroscedastic modeling (HM) for image classification with noise labels. Rather than modeling all images in one fell swoop as in HM, we instead propose a novel framework that considers the noisy and clean samples separately for chest X-ray image classification. The proposed framework consists of a Gaussian Mixture Model-based noise detector and a Heteroscedastic Modeling-based noise-aware classification network, named GMM-HM. The noise detector is constructed to judge whether one sample is clean or noisy. The noise-aware classification network models the noisy and clean samples with heteroscedastic and homoscedastic hypotheses, respectively. Through building the correlations between the corrupted noisy samples, the GMM-HM is much more robust than HM, which uses only the homoscedastic hypothesis. Compared with HM, we show consistent improvements on the ChestX-ray2017 dataset with different levels of symmetric and asymmetric noise. Furthermore, we also conduct experiments on a real asymmetric noisy dataset, ChestX-ray14. The experimental results on ChestX-ray14 show the superiority of the proposed method.
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Arava, Dhanushwi, Seyi Swathhy Yaganti, and Naga Sravani Vemu. "Chest X-Ray Report Analysis." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 1814–19. http://dx.doi.org/10.22214/ijraset.2022.43971.
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Abstract: The detection methods used in x-ray image findings use image classification which gives inaccurate and gives low recognition accuracy. So, we would use an image model feature fusion. This method will take the image and then perform some operations such as rotation, translation etc. Then it reduces training parameters and reduce overfitting of the data. Because when there is overfitting in the data, the results will be in accurate and gives the negative impact on the new data. When the model gets fine-tuned and trained, we would evaluate the trained model and draw a ROC curve. ROC curve is the simple diagnostic test. The closer the apex of the curve to the upper left corner, the greater will be the accuracy. More different experiments tell us that the classification model will give more accuracy to the results than compared to the classic model for the chest x-ray report analysis. After preprocessing the image, we will train the model using the VGG19, RESNET50, DENSENET, INCEPTION and check which among the methods would yield us
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Ismail, Azlan, Taufik Rahmat, and Sharifah Aliman. "CHEST X-RAY IMAGE CLASSIFICATION USING FASTER R-CNN." MALAYSIAN JOURNAL OF COMPUTING 4, no. 1 (2019): 225. http://dx.doi.org/10.24191/mjoc.v4i1.6095.
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Chest x-ray image analysis is the common medical imaging exam needed to assess different pathologies. Having an automated solution for the analysis can contribute to minimizing the workloads, improve efficiency and reduce the potential of reading errors. Many methods have been proposed to address chest x-ray image classification and detection. However, the application of regional-based convolutional neural networks (CNN) is currently limited. Thus, we propose an approach to classify chest x-ray images into either one of two categories, pathological or normal based on Faster Regional-CNN model. This model utilizes Region Proposal Network (RPN) to generate region proposals and perform image classification. By applying this model, we can potentially achieve two key goals, high confidence in the classification and reducing the computation time. The results show the applied model achieved higher accuracy as compared to the medical representatives on the random chest x-ray images. The classification model is also reasonably effective in classifying between finding and normal chest x-ray image captured through a live webcam.
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Ahmed, Md Toukir, and Mohammed Sowket Ali. "Chest X-Ray Examiner." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3166–73. http://dx.doi.org/10.22214/ijraset.2022.42851.
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Abstract: The most common image taken in the medical field for diagnosis of any ailment affecting the chest is chest radiography (Chest X-ray). The most common image taken in the medical field for diagnosis of any ailment affecting the chest or neighboring area is chest radiography (Chest X-ray). The use of this approach has been limited due to a scarcity of qualified radiologists. To address this issue, we are developing a computer-aided diagnosis system for chest X-ray disease classification that employs DNN (Deep Neural Network) Transfer learning. CXE (Chest X-Ray Examiner) is a web-based program that works in conjunction with our machine learning console. The most common image taken in the medical field for diagnosis of any ailment affecting the chest is chest radiography (Chest X-ray). There is a Rest API application that serves as a middleman between our user interface and machine learning applications. We used our machine learning console application produced by ML.NET to train our own model using the Mobile.Net v3 Image categorization method. By obtaining X-Ray images from end users, the Chest X-Ray Examiner (CXE) can classify the chest disease name and forecast the accuracy level of that disease. Keywords: Chest X-ray, DNN, API, ML.NET, Chest X-Ray Examiner
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Kanwal, Anika, and Siva Chandrasekaran. "2dCNN-BiCuDNNLSTM: Hybrid Deep-Learning-Based Approach for Classification of COVID-19 X-ray Images." Sustainability 14, no. 11 (2022): 6785. http://dx.doi.org/10.3390/su14116785.
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The coronavirus (COVID-19) is a major global disaster of humankind, in the 21st century. COVID-19 initiates breathing infection, including pneumonia, common cold, sneezing, and coughing. Initial detection becomes crucial, to classify the virus and limit its spread. COVID-19 infection is similar to other types of pneumonia, and it may result in severe pneumonia, with bundles of illness onsets. This research is focused on identifying people affected by COVID-19 at a very early stage, through chest X-ray images. Chest X-ray classification is a beneficial method in the identification, follow up, and evaluation of treatment efficiency, for people with pneumonia. This research, also, considered chest X-ray classification as a basic method to evaluate the existence of lung irregularities in symptomatic patients, alleged for COVID-19 disease. The aim of this research is to classify COVID-19 samples from normal chest X-ray images and pneumonia-affected chest X-ray images of people, for early identification of the disease. This research will help people in diagnosing individuals for viruses and insisting that people receive proper treatment as well as preventive action, to stop the spread of the virus. To provide accurate classification of disease in patients’ chest X-ray images, this research proposed a novel classification model, named 2dCNN-BiCuDNNLSTM, which combines two-dimensional Convolutional Neural Network (CNN) and a Bidirectional CUDA Deep Neural Network Long Short-Term Memory (BiCuDNNLSTM). Deep learning is known for identifying the patterns in available data that will be helpful in accurate classification of disease. The proposed model (2dCNN and BiCuDNNLSTM layers, with proper hyperparameters) can differentiate normal chest X-rays from viral pneumonia and COVID-19 ones, with high accuracy. A total of 6863 X-ray images (JPEG) (1000 COVID-19 patients, 3863 normal cases, and 2000 pneumonia patients) have been engaged, to examine the achievement of the suggested neural network; 80% of the images dataset for every group is received for proposed model training, 10% is accepted for validation, and 10% is accepted for testing. It is observed that the proposed model acquires the towering classification accuracy of 93%. The proposed network is used for predictive analysis, to prompt people regarding the risk of early detection of COVID-19. X-ray images help to classify people with COVID-19 variants and to indicate the severity of disease in the future. This study demonstrates the effectiveness of the proposed CUDA-enabled hybrid deep learning models, to classify the X-ray image data, with a high accuracy of detecting COVID-19. It reveals that the proposed model can be applicable in numerous virus classifications. The chest X-ray classification is a commonly available and reasonable approach, for diagnosing people with lower respiratory signs or suspected COVID-19. Therefore, it is demonstrated that the proposed model has an efficient and promising accomplishment for classifying COVID-19 through X-ray images. The proposed hybrid model can, efficiently, preserve the comprehensive characteristic facts of the image data, for more exceptional concluding classification results than an individual neural network.
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Badawi, Abeer, and Khalid Elgazzar. "Detecting Coronavirus from Chest X-rays Using Transfer Learning." COVID 1, no. 1 (2021): 403–15. http://dx.doi.org/10.3390/covid1010034.
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Coronavirus disease (COVID-19) is an illness caused by a novel coronavirus family. One of the practical examinations for COVID-19 is chest radiography. COVID-19 infected patients show abnormalities in chest X-ray images. However, examining the chest X-rays requires a specialist with high experience. Hence, using deep learning techniques in detecting abnormalities in the X-ray images is presented commonly as a potential solution to help diagnose the disease. Numerous research has been reported on COVID-19 chest X-ray classification, but most of the previous studies have been conducted on a small set of COVID-19 X-ray images, which created an imbalanced dataset and affected the performance of the deep learning models. In this paper, we propose several image processing techniques to augment COVID-19 X-ray images to generate a large and diverse dataset to boost the performance of deep learning algorithms in detecting the virus from chest X-rays. We also propose innovative and robust deep learning models, based on DenseNet201, VGG16, and VGG19, to detect COVID-19 from a large set of chest X-ray images. A performance evaluation shows that the proposed models outperform all existing techniques to date. Our models achieved 99.62% on the binary classification and 95.48% on the multi-class classification. Based on these findings, we provide a pathway for researchers to develop enhanced models with a balanced dataset that includes the highest available COVID-19 chest X-ray images. This work is of high interest to healthcare providers, as it helps to better diagnose COVID-19 from chest X-rays in less time with higher accuracy.
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Caseneuve, Guy, Iren Valova, Nathan LeBlanc, and Melanie Thibodeau. "Chest X-Ray Image Preprocessing for Disease Classification." Procedia Computer Science 192 (2021): 658–65. http://dx.doi.org/10.1016/j.procs.2021.08.068.
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Wollek, Alessandro, Sardi Hyska, Bastian Sabel, Michael Ingrisch, and Tobias Lasser. "WindowNet: Learnable Windows for Chest X-ray Classification." Journal of Imaging 9, no. 12 (2023): 270. http://dx.doi.org/10.3390/jimaging9120270.
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Public chest X-ray (CXR) data sets are commonly compressed to a lower bit depth to reduce their size, potentially hiding subtle diagnostic features. In contrast, radiologists apply a windowing operation to the uncompressed image to enhance such subtle features. While it has been shown that windowing improves classification performance on computed tomography (CT) images, the impact of such an operation on CXR classification performance remains unclear. In this study, we show that windowing strongly improves the CXR classification performance of machine learning models and propose WindowNet, a model that learns multiple optimal window settings. Our model achieved an average AUC score of 0.812 compared with the 0.759 score of a commonly used architecture without windowing capabilities on the MIMIC data set.
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Markom, M. A., S. Mohd Taha, A. H. Adom, et al. "A Review: Deep Learning Classification Performance of Normal and COVID-19 Chest X-ray Images." Journal of Physics: Conference Series 2071, no. 1 (2021): 012003. http://dx.doi.org/10.1088/1742-6596/2071/1/012003.
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Abstract COVID19 chest X-ray has been used as supplementary tools to support COVID19 severity level diagnosis. However, there are challenges that required to face by researchers around the world in order to implement these chest X-ray samples to be very helpful to detect the disease. Here, this paper presents a review of COVID19 chest X-ray classification using deep learning approach. This study is conducted to discuss the source of images and deep learning models as well as its performances. At the end of this paper, the challenges and future work on COVID19 chest X-ray are discussed and proposed.
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Ucan, Murat, Buket Kaya, Osman Aygun, Mehmet Kaya, and Reda Alhajj. "Comparison of EfficientNet CNN models for multi-label chest X-ray disease diagnosis." PeerJ Computer Science 11 (July 1, 2025): e2968. https://doi.org/10.7717/peerj-cs.2968.
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The analysis of chest X-ray images, which are critical for the early diagnosis of many diseases, is a difficult and time-consuming process due to the multiple labeling requirements and similar looking pathologies. In traditional methods, expert physicians analyze high-resolution chest X-ray images to diagnose these diseases using observational methods, a process that can lead to human error and hence misdiagnosis or underdiagnosis. In this study, we aim to autonomously detect 14 different diseases that significantly affect human health and some cases even lead to death using chest X-ray images in a multi-class manner using deep learning techniques. Previous studies on chest X-ray images focus on a single disease or have low success rates, and the architectures presented in previous studies have high computational costs. The novelty of this work is that it presents a hybrid lightweight, fast and attention-based architecture with high classification performance. In this study, we used the ChestX-Ray14 dataset consisting of 112,104 labeled chest X-ray images of 14 disease classes. Eight deep learning architectures (EfficientNetB0-B7) and coordinate attention mechanism are used in the training and testing processes. The proposed EfficientNetB7 architecture achieved an average overall classification performance with an AUC value of 0.8265. The EfficientNet enhanced with coordinate attention architecture achieved a classification success with an AUC value of 0.8309. Moreover, when the proposed architecture and the individual disease classes are considered separately, higher classification success is achieved for eight of the 14 diseases in the dataset. Finally, the results of this study outperformed the classification performance of other similar studies in the literature in terms of AUC score. The results obtained in our study show that the proposed deep learning based lightweight and fast architecture can support radiologists in decision making in disease diagnosis. The use of autonomous disease diagnosis systems can support the protection of human health by preventing incomplete or erroneous diagnoses.
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Gao, Xinyue, Bo Jiang, Xixi Wang, Lili Huang, and Zhengzheng Tu. "Chest x-ray diagnosis via spatial-channel high-order attention representation learning." Physics in Medicine & Biology 69, no. 4 (2024): 045026. http://dx.doi.org/10.1088/1361-6560/ad2014.
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Abstract Objective. Chest x-ray image representation and learning is an important problem in computer-aided diagnostic area. Existing methods usually adopt CNN or Transformers for feature representation learning and focus on learning effective representations for chest x-ray images. Although good performance can be obtained, however, these works are still limited mainly due to the ignorance of mining the correlations of channels and pay little attention on the local context-aware feature representation of chest x-ray image. Approach. To address these problems, in this paper, we propose a novel spatial-channel high-order attention model (SCHA) for chest x-ray image representation and diagnosis. The proposed network architecture mainly contains three modules, i.e. CEBN, SHAM and CHAM. To be specific, firstly, we introduce a context-enhanced backbone network by employing multi-head self-attention to extract initial features for the input chest x-ray images. Then, we develop a novel SCHA which contains both spatial and channel high-order attention learning branches. For the spatial branch, we develop a novel local biased self-attention mechanism which can capture both local and long-range global dependences of positions to learn rich context-aware representation. For the channel branch, we employ Brownian Distance Covariance to encode the correlation information of channels and regard it as the image representation. Finally, the two learning branches are integrated together for the final multi-label diagnosis classification and prediction. Main results. Experiments on the commonly used datasets including ChestX-ray14 and CheXpert demonstrate that our proposed SCHA approach can obtain better performance when comparing many related approaches. Significance. This study obtains a more discriminative method for chest x-ray classification and provides a technique for computer-aided diagnosis.
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Nandhini Abirami, R., P. M. Durai Raj Vincent, Venkatesan Rajinikanth, and Seifedine Kadry. "COVID-19 Classification Using Medical Image Synthesis by Generative Adversarial Networks." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 30, no. 03 (2022): 385–401. http://dx.doi.org/10.1142/s0218488522400128.
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The outbreak of novel coronavirus disease 2019, also called COVID-19, in Wuhan, China, began in December 2019. Since its outbreak, infectious disease has rapidly spread across the globe. The testing methods adopted by the medical practitioners gave false negatives, which is a big challenge. Medical imaging using deep learning can be adopted to speed up the testing process and avoid false negatives. This work proposes a novel approach, COVID-19 GAN, to perform coronavirus disease classification using medical image synthesis by a generative adversarial network. Detecting coronavirus infections from the chest X-ray images is very crucial for its early diagnosis and effective treatment. To boost the performance of the deep learning model and improve the accuracy of classification, synthetic data augmentation is performed using generative adversarial networks. Here, the available COVID-19 positive chest X-ray images are fed into the styleGAN2 model. The styleGAN model is trained, and the data necessary for training the deep learning model for coronavirus classification is generated. The generated COVID-19 positive chest X-ray images and the normal chest X-ray images are fed into the deep learning model for training. An accuracy of 99.78% is achieved in classifying chest X-ray images using CNN binary classifier model.
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Mogaveera, Rachita, Roshan Maur, Zeba Qureshi, and Yogita Mane. "Multi-class Chest X-ray classification of Pneumonia, Tuberculosis and Normal X-ray images using ConvNets." ITM Web of Conferences 44 (2022): 03007. http://dx.doi.org/10.1051/itmconf/20224403007.
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Pneumonia and Tuberculosis (TB) are two serious and life-threatening diseases that are caused by a bacterial or viral infection of the lungs and have the potential to result in severe consequences within a short period of time. Therefore, early diagnosis is a significant factor in terms of a successful treatment process. Chest X-Rays which are used to diagnose Pneumonia and/or Tuberculosis need expert radiologists for evaluation. Thus, there is a need for an intelligent and automatic system that has the capability of diagnosing chest X-rays, and to simplify the disease detection process for experts and novices. This study aims to develop a model that will help with the classification of chest X-ray medical images into normal vs Pneumonia or Tuberculosis. Medical organizations take a minimum of one day to classify the diagnosis, while our model could perform the same classification within a few seconds. Also, it will display a prediction probability about the predicted class. The model had an accuracy, precision and recall score over 90% which indicates that the model was able to identify patterns. Users can upload their respective chest X-ray image and the model will classify the uploaded image into normal vs abnormal.
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D, L. Asha Rani, Anishiya P, and Pramananda Perumal T. "Efficient Detection of Covid-19 from Chest X-ray Images using CNN Feature Extraction and an Ensemble of Machine Learning Classifiers." Indian Journal of Science and Technology 16, no. 38 (2023): 3303–15. https://doi.org/10.17485/IJST/v16i38.1888.
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Abstract <strong>Objectives:</strong> The main objective of this work is to detect Covid-19 in radiological chest X-ray images, using Convolutional Neural Network (CNN) as a feature extractor and classify the CNN block-wise features extracted using an ensemble of Machine Learning (ML) classifiers. The classifications of radiological (chest X-ray) images into binary class (Covid-19 and Non-Covid-19) and multi-class (Lungs infected by Covid-19, Normal Lungs and Lungs infected by Pneumonia) are performed in this work. <strong>Methods:</strong> The various six CNN pre-trained models viz. AlexNet, GoogleNet, VGG-16, ResNet-50, SqueezeNet and Inception-V3 and our proposed CoronaNet model are used for feature extraction. Four most popular ML classifiers such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Decision Tree (DT) and Naive-Bayes (NB) are used to classify the features extracted from each of the CNN pre-trained and proposed CoronaNet models. The public dataset of chest X-ray images, created by Joseph Paul Cohen and retrieved from GitHub (Covid-19 - Chest X-ray images dataset) is used in our research work. In total, 3785 training samples, 1686 validation samples and 150 testing samples are used in this work. <strong>Findings:</strong> The comparative analysis shows that the proposed CoronaNet model with SVM ML classifier has achieved the highest classification accuracy of 97.7% for binary class classification and 96.6% for multi-class classification. <strong>Novelty:</strong> Exhaustive block wise analysis of the CNN features from the six most popular CNN pre-trained models and the proposed CoronaNet model shows that extracted features in the last layer of each preceding block of CNN models + SVM classifier have resulted in improved classification accuracies, when it is compared to that in the FC /Pool10 layer of CNN models + SVM or Softmax classifier. <strong>Keywords:</strong> CNN Deep learning, Machine Learning, Feature extraction, Chest X-ray Images Classification, Covid-19
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Roopa, H., and T. Asha. "Feature Extraction of Chest X-ray Images and Analysis Using PCA and kPCA." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3392–98. https://doi.org/10.11591/ijece.v8i5.pp3392-3398.
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Tuberculosis (TB) is an infectious disease caused by mycobacterium which can be diagnosed by its various symptoms like fever, cough, etc. Tuberculosis can also be analyzed by understanding the chest x-ray of the patient which is revealed by an expert physician .The chest x-ray image contains many features which cannot be directly used by any computer system for analyzing the disease. Features of chest x-ray images must be understood and extracted, so that it can be processed to a form to be fed to any computer system for disease analysis. This paper presents feature extraction of chest x-ray image which can be used as an input for any data mining algorithm for TB disease analysis. So texture and shape based features are extracted from x-ray image using image processing concepts. The features extracted are analyzed using principal component analysis (PCA) and kernel principal component analysis (kPCA) techniques. Filter and wrapper feature selection method using linear regression model were applied on these techniques. The performance of PCA and kPCA are analyzed and found that the accuracy of PCA using wrapper approach is 96.07% when compared to the accuracy of kPCA which is 62.50%. PCA performs well than kPCA with a good accuracy.
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Nirdesh Jain and Dr. Aditya Mandloi. "Machine Learning Based X-RAY Prediction Model." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 05 (2024): 1361–64. http://dx.doi.org/10.47392/irjaem.2024.0187.
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This study aimed to develop and evaluate a convolutional neural network (CNN) model for multi-disease classification using a large dataset of 53,000+ chest X-ray images. The CNN architecture was trained to predict the presence of 14 different diseases based on input chest X-ray images. Key findings indicate the model achieves competitive performance with high accuracy, demonstrating potential for automated disease diagnosis. Leveraging the power of deep learning, particularly CNNs, this study shows promising results in improving diagnostic processes in healthcare. Automating disease diagnosis using deep learning methods can significantly enhance the efficiency of healthcare systems, potentially reducing the burden on medical professionals and improving patient outcomes. The success of this CNN model in multi-disease classification based on chest X-ray images highlights the potential of artificial intelligence in revolutionizing diagnostic processes in healthcare, underscoring the importance and effectiveness of deep learning methods, particularly CNNs, in advancing medical diagnostics and improving patient care.
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Naskinova, I. "On Convolutional Neural Networks for Chest X-ray Classification." IOP Conference Series: Materials Science and Engineering 1031, no. 1 (2021): 012075. http://dx.doi.org/10.1088/1757-899x/1031/1/012075.
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Adi, Prajanto Wahyu, Fajar Agung Nugroho, and Yani Parti Astuti. "New Image Texture Feature for Chest X-Ray Classification." Journal of Applied Intelligent System 7, no. 1 (2022): 8–15. http://dx.doi.org/10.33633/jais.v7i1.5340.
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This study proposes a new feature extraction model to identify CXR images of covid-19 and pneumonia has a high visual resemblance. The feature extraction model starts by using histogram equalization and average filters as lowpass features and high pass features obtained through Laplacian and LoG filters. In the next step, covariance matrix of image along with the entire features are used to produce an eigen vector that will be used as a feature vector in the classification process. The final stage is the process of testing features on the classification algorithms KNN, SVM, LDA, Naïve Bayes, and Decision Tree through a 10-foldcross validation scheme with 0.9 training data and 0.1 test data. The first experiment for the Covid-19 and normal classes shows that the proposed model is able to produce an accuracy of 96% as the comparison model with GLCM texture extraction have an accuracy value of 91%. The second test is conducted for the class Covid-19 and pneumonia and obtained an accuracy value of 89% for the proposed model and 73% for the GLCM texture extraction. Experiments proved that the proposed model successfully outperformed the GLCM texture extraction model in all of classification algorithms used.
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Sood, Neetu, Indu Saini, and Ruchika Arora. "Chest x-ray image analysis for pneumonitis disease classification." International Journal of Medical Engineering and Informatics 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijmei.2022.10047778.
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Giełczyk, Agata, Anna Marciniak, Martyna Tarczewska, and Zbigniew Lutowski. "Pre-processing methods in chest X-ray image classification." PLOS ONE 17, no. 4 (2022): e0265949. http://dx.doi.org/10.1371/journal.pone.0265949.
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Background The SARS-CoV-2 pandemic began in early 2020, paralyzing human life all over the world and threatening our security. Thus, the need for an effective, novel approach to diagnosing, preventing, and treating COVID-19 infections became paramount. Methods This article proposes a machine learning-based method for the classification of chest X-ray images. We also examined some of the pre-processing methods such as thresholding, blurring, and histogram equalization. Results We found the F1-score results rose to 97%, 96%, and 99% for the three analyzed classes: healthy, COVID-19, and pneumonia, respectively. Conclusion Our research provides proof that machine learning can be used to support medics in chest X-ray classification and improving pre-processing leads to improvements in accuracy, precision, recall, and F1-scores.
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Arora, Ruchika, Indu Saini, and Neetu Sood. "Chest X-ray image analysis for pneumonitis disease classification." International Journal of Medical Engineering and Informatics 16, no. 5 (2024): 414–23. http://dx.doi.org/10.1504/ijmei.2024.140796.
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Pillai, Aravind Sasidharan. "Multi-Label Chest X-Ray Classification via Deep Learning." Journal of Intelligent Learning Systems and Applications 14, no. 04 (2022): 43–56. http://dx.doi.org/10.4236/jilsa.2022.144004.
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Shin, Heejun, Taehee Kim, Hruthvik Raj, Muhammad Shahid Jabbar, Zeleke Desalegn Abebaw, and Dongmyung Shin. "MACHINE-INDEPENDENT AI FOR CHEST X-RAY ABNORMALITY CLASSIFICATION." Journal of Medical Imaging and Radiation Sciences 54, no. 3 (2023): S12. http://dx.doi.org/10.1016/j.jmir.2023.06.044.
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Yuliawan, Endra, and Shofwatul ‘Uyun. "Chest X-ray Image Classification for COVID-19 diagnoses." Journal of Information Systems Engineering and Business Intelligence 8, no. 2 (2022): 109–18. http://dx.doi.org/10.20473/jisebi.8.2.109-118.
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Background: Radiologists used chest radiographs to detect coronavirus disease 2019 (COVID-19) in patients and determine the severity levels. The COVID-19 cases were grouped into five classes, each receiving different treatments. An intelligent system is needed to advance the detection and identify vector features of X-ray images with a quality that is too poor to be read by radiologists. Deep learning is an intelligent system that can be used in this case. Objective: The current study compares the classification and accuracy of detection methods with two, three dan five classes. Methods: Deep learning can classify visual geometry group VGG 19 architectures with 1000 classes. The classification of the five classes' convolutional neural network (CNN) underwent model validation with a confusion matrix to produce accuracy and class values. The system could then diagnose patients’ examinations by radiology specialists. Results: The results of the five-class method showed 98% accuracy, the three-class method showed 99.99%, and the two-class showed 99.99%. Conclusion: It can be concluded that using the VGG 19 model is effective. This system can classify and diagnose viruses in patients to assist radiologists by reading the images. Keywords: COVID-19, CNN, Classification, Deep Learning
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Gatti, Marco, Jessica Amianto Barbato, and Claudio Zandron. "Spiking neural network classification of X-ray chest images." Knowledge-Based Systems 314 (April 2025): 113194. https://doi.org/10.1016/j.knosys.2025.113194.
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Rocha, Joana, Ana Maria Mendonça, and Aurélio Campilho. "Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification." U.Porto Journal of Engineering 7, no. 4 (2021): 16–32. http://dx.doi.org/10.24840/2183-6493_007.004_0002.
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Backed by more powerful computational resources and optimized training routines, deep learning models have proven unprecedented performance and several benefits to extract information from chest X-ray data. This is one of the most common imaging exams, whose increasing demand is reflected in the aggravated radiologists’ workload. Consequently, healthcare would benefit from computer-aided diagnosis systems to prioritize certain exams and further identify possible pathologies. Pioneering work in chest X-ray analysis has focused on the identification of specific diseases, but to the best of the authors' knowledge no paper has specifically reviewed relevant work on abnormality detection and multi-label thoracic pathology classification. This paper focuses on those issues, selecting the leading chest X-ray based deep learning strategies for comparison. In addition, the paper discloses the current annotated public chest X-ray databases, covering the common thorax diseases.
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Denis, Eka Cahyani, Dwi Hariadi Anjar, Farris Setyawan Faisal, Gumilar Langlang, and Setumin Samsul. "COVID-19 classification using CNN-BiLSTM based on chest X-ray images." Bulletin of Electrical Engineering and Informatics 12, no. 3 (2023): 1773~1782. https://doi.org/10.11591/eei.v12i3.4848.
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Cases of the COVID-19 virus continue to spread still needs to be considered even though we have entered the post-pandemic era. Rapid identification of COVID-19 cases is necessary to prevent the virus from spreading further. This study developed a chest X-ray-based (CXR) COVID-19 classification for COVID-19 detection using the convolutional neural networkbidirectional long short-term memory (CNN-BiLSTM) combination model and compared the CNN-BiLSTM combination model with CNN models. The CNN models used in this study are the transfer learning models, namely Resnet50, VGG19, InceptionV3, Xception, and AlexNet. This research classifies CXR into three groups: COVID-19, normal, and viral pneumonia. In comparison to other models, the Resnet50-BiLSTM model is the most accurate and hence the best. The accuracy of the Resnet50-BiLSTM model was 98.48%. The model that obtains the next highest accuracy i.e Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, InceptionV3, XceptionBiLSTM, Xception, AlexNet-BiLSTM, and AlexNet. In this study, precision, recall, and F1-measure are also employed to demonstrate that Resnet50-BiLSTM achieves the highest value compared to other approaches. When compared to previous studies, this study enhances classification performance results.
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Fauzan, Ahmad Rafiansyah, Mohammad Iwan Wahyuddin, and Sari Ningsih. "Pleural Effusion Classification Based on Chest X-Ray Images using Convolutional Neural Network." Jurnal Ilmu Komputer dan Informasi 14, no. 1 (2021): 9–16. http://dx.doi.org/10.21609/jiki.v14i1.898.
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Pleural effusion is a respiratory infection characterized by a buildup of fluid between the two layers of pleura, which causes specific symptoms such as chest pain and shortness of breath. In Indonesia, pleural effusion cases alone account for 2.7% of other respiratory infections, with an estimated number of sufferers in general at more than 3000 people per 1 million population annually. Pleural effusion is a severe case and can cause death if not treated immediately. Based on a study, as many as 15% of 104 patients diagnosed with pleural effusion died within 30 days. In this paper, we present a model that can detect pleural effusion based on chest x-ray images automatically using a Machine Learning algorithm. The machine learning algorithm used is Convolutional Neural Network (CNN), with the dataset used from ChestX-ray14. The number of data used was 2500 in the form of x-ray images, based on two different classes, x-ray with pleural effusion and x-ray with normal condition. The evaluation result shows that the CNN model can classify data with an accuracy of 95% of the test set data; thus, we hope it can be an alternative to assist medical diagnosis in pleural effusion detection.
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Ko, Doo-Hyeon, and Se-woon Choe. "Exploring Deep Learning-Based COVID-19 Chest X-ray Image Classification Models." Journal of the Korea Institute of Information and Communication Engineering 27, no. 11 (2023): 1351–58. http://dx.doi.org/10.6109/jkiice.2023.27.11.1351.
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Aljuaid, Hanan, Hessa Albalahad, Walaa Alshuaibi, et al. "RADAI: A Deep Learning-Based Classification of Lung Abnormalities in Chest X-Rays." Diagnostics 15, no. 13 (2025): 1728. https://doi.org/10.3390/diagnostics15131728.
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Background: Chest X-rays are rapidly gaining prominence as a prevalent diagnostic tool, as recognized by the World Health Organization (WHO). However, interpreting chest X-rays can be demanding and time-consuming, even for experienced radiologists, leading to potential misinterpretations and delays in treatment. Method: The purpose of this research is the development of a RadAI model. The RadAI model can accurately detect four types of lung abnormalities in chest X-rays and generate a report on each identified abnormality. Moreover, deep learning algorithms, particularly convolutional neural networks (CNNs), have demonstrated remarkable potential in automating medical image analysis, including chest X-rays. This work addresses the challenge of chest X-ray interpretation by fine tuning the following three advanced deep learning models: Feature-selective and Spatial Receptive Fields Network (FSRFNet50), ResNext50, and ResNet50. These models are compared based on accuracy, precision, recall, and F1-score. Results: The outstanding performance of RadAI shows its potential to assist radiologists to interpret the detected chest abnormalities accurately. Conclusions: RadAI is beneficial in enhancing the accuracy and efficiency of chest X-ray interpretation, ultimately supporting the timely and reliable diagnosis of lung abnormalities.
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Kalaiselvi, K., and M. Kasthuri. "Refining the Accuracy of Chest X-Ray Image Classification Through Layer and Activation Function Optimization." Indian Journal Of Science And Technology 16, no. 13 (2023): 1030–37. http://dx.doi.org/10.17485/ijst/v16i13.138.
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Dhawan, Kunaal, and Siddharth S Nijhawan. "Cross-Modality Synthetic Data Augmentation using GANs: Enhancing Brain MRI and Chest X-Ray Classification." International Journal of Science and Research (IJSR) 14, no. 1 (2025): 524–32. https://doi.org/10.21275/sr25105190913.
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Xu, Jing, Hui Li, and Xiu Li. "MS-ANet: deep learning for automated multi-label thoracic disease detection and classification." PeerJ Computer Science 7 (May 17, 2021): e541. http://dx.doi.org/10.7717/peerj-cs.541.
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The chest X-ray is one of the most common radiological examination types for the diagnosis of chest diseases. Nowadays, the automatic classification technology of radiological images has been widely used in clinical diagnosis and treatment plans. However, each disease has its own different response characteristic receptive field region, which is the main challenge for chest disease classification tasks. Besides, the imbalance of sample data categories further increases the difficulty of tasks. To solve these problems, we propose a new multi-label chest disease image classification scheme based on a multi-scale attention network. In this scheme, multi-scale information is iteratively fused to focus on regions with a high probability of disease, to effectively mine more meaningful information from data. A novel loss function is also designed to improve the rationality of visual perception and multi-label image classification, which forces the consistency of attention regions before and after image transformation. A comprehensive experiment was carried out on the Chest X-Ray14 and CheXpert datasets, separately containing over 100,000 frontal-view and 200,000 front and side view X-ray images with 14 diseases. The AUROC is 0.850 and 0.815 respectively on the two data sets, which achieve the state-of-the-art results, verified the effectiveness of this method in chest X-ray image classification. This study has important practical significance for using AI algorithms to assist radiologists in improving work efficiency and diagnostic accuracy.
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Maksum, Vivin Umrotul M., Dian C. Rini Novitasari, and Abdulloh Hamid. "Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM." Jurnal Matematika MANTIK 7, no. 1 (2021): 74–85. http://dx.doi.org/10.15642/mantik.2021.7.1.74-85.
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COVID-19 is a disease or virus that has recently spread worldwide. The disease has also taken many casualties because the virus is notoriously deadly. An examination can be carried out using a chest X-Ray because it costs cheaper compared to swab and PCR tests. The data used in this study was chest X-Ray image data. Chest X-Ray images can be identified using Computer-Aided Diagnosis by utilizing machine learning classification. The first step was the preprocessing stage and feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). The result of the feature extraction was then used at the classification stage. The classification process used was Extreme Learning Machine (ELM). Extreme Learning Machine (ELM) is one of the artificial neural networks with advanced feedforward which has one hidden layer called Single Hidden Layer Feedforward Neural Networks (SLFNs). The results obtained by GLCM feature extraction and classification using ELM achieved the best accuracy of 91.21%, the sensitivity of 100%, and the specificity of 91% at 135° rotation using linear activation function with 15 hidden nodes.
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Huang, Guan-Hua, Qi-Jia Fu, Ming-Zhang Gu, Nan-Han Lu, Kuo-Ying Liu, and Tai-Been Chen. "Deep Transfer Learning for the Multilabel Classification of Chest X-ray Images." Diagnostics 12, no. 6 (2022): 1457. http://dx.doi.org/10.3390/diagnostics12061457.
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Chest X-ray (CXR) is widely used to diagnose conditions affecting the chest, its contents, and its nearby structures. In this study, we used a private data set containing 1630 CXR images with disease labels; most of the images were disease-free, but the others contained multiple sites of abnormalities. Here, we used deep convolutional neural network (CNN) models to extract feature representations and to identify possible diseases in these images. We also used transfer learning combined with large open-source image data sets to resolve the problems of insufficient training data and optimize the classification model. The effects of different approaches of reusing pretrained weights (model finetuning and layer transfer), source data sets of different sizes and similarity levels to the target data (ImageNet, ChestX-ray, and CheXpert), methods integrating source data sets into transfer learning (initiating, concatenating, and co-training), and backbone CNN models (ResNet50 and DenseNet121) on transfer learning were also assessed. The results demonstrated that transfer learning applied with the model finetuning approach typically afforded better prediction models. When only one source data set was adopted, ChestX-ray performed better than CheXpert; however, after ImageNet initials were attached, CheXpert performed better. ResNet50 performed better in initiating transfer learning, whereas DenseNet121 performed better in concatenating and co-training transfer learning. Transfer learning with multiple source data sets was preferable to that with a source data set. Overall, transfer learning can further enhance prediction capabilities and reduce computing costs for CXR images.
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Sheikh, A. Z., Z. Tariq, S. Noor, A. Ambreen, S. Awan, and M. Amjad. "Chest X-Rays Findings in Patients Positive for COVID 19 at Sheikh Zayed Hospital Lahore." Pakistan Journal of Medical and Health Sciences 15, no. 5 (2021): 1196–99. http://dx.doi.org/10.53350/pjmhs211551196.
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Aim: To assess the results of chest x ray radiographs of patients positive for Covid-19, presented at the tertiary care hospital according to the classification by the British Society of Thoracic Imaging (BSTI. Place and Duration: In COVID-19 Ward (Department of Medicine) Sheikh Zayed Hospital, Lahore for three months duration from January 2021 to March 2021. Methods: A total of 96 patients were selected. In this observational study, positive COVID-19 patient determined by the reverse transcriptase polymerase chain reaction (RT-PCR) were enrolled for this study above the age of 14 years. CXR results were classified conferring to BSTI documentation and classification in terms of percentage and frequency. Results: Chest rays of 96 patients who tested positive for Covid-19 by RT-PCR over the age of 14 years were examined. Chest X-rays are classified according to the BSTI Covid-19 X-ray classification. Out of 96 patients, 10 patients (10.41%) had normal chest x-rays, 19 (19.80%) patients had classic bilateral, peripheral and basal consolidation / ground glass opacity (GMO), 60 (62.5%) had unspecified group,7(7.29%) patients have poor quality X-ray film. The unilateral involvement was noticed in 15 and bilateral in 49 patients, 12 of the patients had diffuse involvement on chest radiograph and peripheral involvement in 39 patients. According to regional dominance, 41 of the unspecified (42.70%) had middle and lower lung involvement, 7 (7.29%) had only the middle zone, and 8 (8.33%) had involvement of lower zone. Conclusions: In this study, Covid-19 chest X-rays are usually presented as ground glass opacity, mixed consolidation with GGOs in the middle and lower peripheral areas of the bilateral lung. Chest X-ray BSTI classification is used to classify Covid-19 severity in our patients, thus differentiating in the classic Covid-19 of the middle zone versus low zone involvement. Keywords: Consolidation, Covid, Ground Glass Opacity, Chest Image.
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Liu, Shaobo, Frank Y. Shih, and Xin Zhong. "Classification of Chest X-Ray Images Using Novel Adaptive Morphological Neural Networks." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 10 (2021): 2157006. http://dx.doi.org/10.1142/s0218001421570068.
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The chest X-ray images are difficult to classify for the radiologists due to the noisy nature. The existing models based on convolutional neural networks contain a giant number of parameters, and thus require multi-advanced GPUs to deploy. In this paper, we are the first to develop the adaptive morphological neural networks to classify chest X-ray images, such as pneumonia and COVID-19. A novel structure, which can self-learn morphological dilation and erosion, is proposed to determine the most suitable depth of the adaptive layer. Experimental results on the chest X-ray and the COVID-19 datasets show that the proposed model can achieve the highest classification rate as compared against the existing models. Moreover, it can significantly reduce the computational parameters of the existing models by 97%. The advantage makes the developed model more attractive than others to deploy in the internet and other device platforms.
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Aydın Atasoy, Nesrin, and İrem Kura. "CLASSIFICATION OF X-RAY AND CT IMAGES IN DIFFERENT COLOR SPACES USING ROBUST CNN." Mühendislik Bilimleri ve Tasarım Dergisi 12, no. 3 (2024): 505–16. http://dx.doi.org/10.21923/jesd.1415150.
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Since deep learning models have been successfully used in many fields, they have been used to identify sick and healthy people in X-ray or Computed Tomography (CT) chest radiology images. In this study, Covid-19 and pneumonia classification is performed on both X-ray and CT images using the robust Convolutional Neural Network (CNN). BGR, HSV, and CIE LAB color space transformations are applied to X-ray and CT images to show that the model performs a successful classification independent of dataset characteristics. The binary classification accuracy rates of Covid-19 and pneumonia for X-ray images and CT images are 98.7% and 98.4%, 97.6% and 99.4%, respectively. Precision, Recall, Specificity, F1 score, and Mean Squared Error metrics are calculated for each X-ray and CT dataset. In addition, 5-fold cross-validation proved accuracy of the model. Although X-ray and CT chest radiology images are transformed into different color spaces, the proposed model performed a successful classification. Thus, even if the image characteristics of the radiology device brands change, the computer-based system will be able to make successful disease diagnoses at low cost where expert personnel are insufficient.
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Cheah, Yew Fai. "Chest X-Ray Classification of Lung Diseases Using Deep Learning." Green Intelligent Systems and Applications 1, no. 1 (2021): 12–18. http://dx.doi.org/10.53623/gisa.v1i1.32.
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Chest X-ray images can be used to detect lung diseases such as COVID-19, viral pneumonia, and tuberculosis (TB). These diseases have similar patterns and diagnoses, making it difficult for clinicians and radiologists to differentiate between them. This paper uses convolutional neural networks (CNNs) to diagnose lung disease using chest X-ray images obtained from online sources. The classification task is separated into three and four classes, with COVID-19, normal, TB, and viral pneumonia, while the three-class problem excludes the normal lung. During testing, AlexNet and ResNet-18 gave promising results, scoring more than 95% accuracy.
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Badr, Malek, Shaha Al-Otaibi, Nazik Alturki, and Tanvir Abir. "Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays." BioMed Research International 2022 (July 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/7833516.
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X-ray images aid medical professionals in the diagnosis and detection of pathologies. They are critical, for example, in the diagnosis of pneumonia, the detection of masses, and, more recently, the detection of COVID-19-related conditions. The chest X-ray is one of the first imaging tests performed when pathology is suspected because it is one of the most accessible radiological examinations. Deep learning-based neural networks, particularly convolutional neural networks, have exploded in popularity in recent years and have become indispensable tools for image classification. Transfer learning approaches, in particular, have enabled the use of previously trained networks’ knowledge, eliminating the need for large data sets and lowering the high computational costs associated with this type of network. This research focuses on using deep learning-based neural networks to detect anomalies in chest X-rays. Different convolutional network-based approaches are investigated using the ChestX-ray14 database, which contains over 100,000 X-ray images with labels relating to 14 different pathologies, and different classification objectives are evaluated. Starting with the pretrained networks VGG19, ResNet50, and Inceptionv3, networks based on transfer learning are implemented, with different schemes for the classification stage and data augmentation. Similarly, an ad hoc architecture is proposed and evaluated without transfer learning for the classification objective with more examples. The results show that transfer learning produces acceptable results in most of the tested cases, indicating that it is a viable first step for using deep networks when there are not enough labeled images, which is a common problem when working with medical images. The ad hoc network, on the other hand, demonstrated good generalization with data augmentation and an acceptable accuracy value. The findings suggest that using convolutional neural networks with and without transfer learning to design classifiers for detecting pathologies in chest X-rays is a good idea.
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Monday, Happy Nkanta, Jianping Li, Grace Ugochi Nneji, et al. "COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network." Diagnostics 12, no. 3 (2022): 741. http://dx.doi.org/10.3390/diagnostics12030741.
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Chest X-ray (CXR) is becoming a useful method in the evaluation of coronavirus disease 19 (COVID-19). Despite the global spread of COVID-19, utilizing a computer-aided diagnosis approach for COVID-19 classification based on CXR images could significantly reduce the clinician burden. There is no doubt that low resolution, noise and irrelevant annotations in chest X-ray images are a major constraint to the performance of AI-based COVID-19 diagnosis. While a few studies have made huge progress, they underestimate these bottlenecks. In this study, we propose a super-resolution-based Siamese wavelet multi-resolution convolutional neural network called COVID-SRWCNN for COVID-19 classification using chest X-ray images. Concretely, we first reconstruct high-resolution (HR) counterparts from low-resolution (LR) CXR images in order to enhance the quality of the dataset for improved performance of our model by proposing a novel enhanced fast super-resolution convolutional neural network (EFSRCNN) to capture texture details in each given chest X-ray image. Exploiting a mutual learning approach, the HR images are passed to the proposed Siamese wavelet multi-resolution convolutional neural network to learn the high-level features for COVID-19 classification. We validate the proposed COVID-SRWCNN model on public-source datasets, achieving accuracy of 98.98%. Our screening technique achieves 98.96% AUC, 99.78% sensitivity, 98.53% precision, and 98.86% specificity. Owing to the fact that COVID-19 chest X-ray datasets are low in quality, experimental results show that our proposed algorithm obtains up-to-date performance that is useful for COVID-19 screening.
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Le Dinh, Tuan, Suk-Hwan Lee, Seong-Geun Kwon, and Ki-Ryong Kwon. "COVID-19 Chest X-ray Classification and Severity Assessment Using Convolutional and Transformer Neural Networks." Applied Sciences 12, no. 10 (2022): 4861. http://dx.doi.org/10.3390/app12104861.
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The coronavirus pandemic started in Wuhan, China in December 2019, and put millions of people in a difficult situation. This fatal virus spread to over 227 countries and the number of infected patients increased to over 400 million cases, causing over 6 million deaths worldwide. Due to the serious consequence of this virus, it is necessary to develop a detection method that can respond quickly to prevent the spreading of COVID-19. Using chest X-ray images to detect COVID-19 is one of the promising techniques; however, with a large number of COVID-19 infected cases every day, the number of radiologists available to diagnose the chest X-ray images is not sufficient. We must have a computer aid system that helps doctors instantly and automatically determine COVID-19 cases. Recently, with the emergence of deep learning methods applied for medical and biomedical uses, using convolutional neural net and transformer applications for chest X-ray images can be a supplement for COVID-19 testing. In this paper, we attempt to classify three types of chest X-ray, which are normal, pneumonia, and COVID-19 using deep learning methods on a customized dataset. We also carry out an experiment on the COVID-19 severity assessment task using a tailored dataset. Five deep learning models were obtained to conduct our experiments: DenseNet121, ResNet50, InceptionNet, Swin Transformer, and Hybrid EfficientNet-DOLG neural networks. The results indicated that chest X-ray and deep learning could be reliable methods for supporting doctors in COVID-19 identification and severity assessment tasks.
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Ragab, Mahmoud, Samah Alshehri, Nabil A. Alhakamy, Romany F. Mansour, and Deepika Koundal. "Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network." Computational Intelligence and Neuroscience 2022 (May 19, 2022): 1–8. http://dx.doi.org/10.1155/2022/6185013.
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It is critical to establish a reliable method for detecting people infected with COVID-19 since the pandemic has numerous harmful consequences worldwide. If the patient is infected with COVID-19, a chest X-ray can be used to determine this. In this work, an X-ray showing a COVID-19 infection is classified by the capsule neural network model we trained to recognise. 6310 chest X-ray pictures were used to train the models, separated into three categories: normal, pneumonia, and COVID-19. This work is considered an improved deep learning model for the classification of COVID-19 disease through X-ray images. Viewpoint invariance, fewer parameters, and better generalisation are some of the advantages of CapsNet compared with the classic convolutional neural network (CNN) models. The proposed model has achieved an accuracy greater than 95% during the model’s training, which is better than the other state-of-the-art algorithms. Furthermore, to aid in detecting COVID-19 in a chest X-ray, the model could provide extra information.
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Wu, Huaiguang, Pengjie Xie, Huiyi Zhang, Daiyi Li, and Ming Cheng. "Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 2893–907. http://dx.doi.org/10.3233/jifs-191438.
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The chest X-ray examination is one of the most important methods for screening and diagnosing of many lung diseases. Diagnosis of pneumonia by chest X-ray is one of the common methods used by medical experts. However, the image quality of chest X-Ray has some defects, such as low contrast, overlapping organs and blurred boundary, which seriously affects detecting pneumonia in chest X-rays. Therefore, it has important medical value and application significance to construct a stable and accurate automatic detection model of pneumonia through a large number of chest X-ray images. In this paper, we propose a novel hybrid system for detecting pneumonia from chest X-Ray image: ACNN-RF, which is an adaptive median filter Convolutional Neural Network (CNN) recognition model based on Random forest (RF). Firstly, the improved adaptive median filtering is employed to remove noise in the chest X-ray image, which makes the image more easily recognized. Secondly, we establish the CNN architecture based on Dropout to extract deep activation features from each chest X-ray image. Finally, we employ the RF classifier based on GridSearchCV class as a classifier for deep activation features in CNN model. It not only avoids the phenomenon of over-fitting in data training, but also improves the accuracy of image classification. During our experiment, the public chest X-ray image dataset used in the experiment contains 5863 images, which comprises 4265 frontal-view X-ray images of 1574 unique patients. The average recognition rate of pneumonia is up to 97% by the proposed ACNN-RF. The experimental results show that the ACNN-RF identification system is more effective than the previous traditional image identification system.
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R, Mr Saikiran,. "Pneumonia Detection Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33887.
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Lung disease called pneumonia is brought on by a bacterial infection. An effective treatment plan heavily depends on an early diagnosis. An expert radiologists can typically diagnose the condition with a chest x-ray. For several reasons, including the emergence of an illness that is not visible on a chest x-ray or the possibility that it is mistaken for another ailment, the diagnosis may be arbitrary. Thus, to assist clinicians, automated diagnostic technologies are required. In this work, a deep learning system for picture classification the Convolutional Neural Networks algorithm was constructed on a dataset. Chest radiographs are included in the data set, and the created model is assessed using a few statistical factors. Key Words: Diagnosis, computer-aided, deep learning, pneumonia, convolution neural networks, mass chest x-ray, chest x-ray14.
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Xu, Shuaijing, Junqi Guo, Guangzhi Zhang, and Rongfang Bie. "Automated Detection of Multiple Lesions on Chest X-ray Images: Classification Using a Neural Network Technique with Association-Specific Contexts." Applied Sciences 10, no. 5 (2020): 1742. http://dx.doi.org/10.3390/app10051742.
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Automated detection of lung lesions on Chest X-ray images shows good performance to reduce lung cancer mortality. However, it is difficult to detect multiple lesions of single image well and truly, and additional efforts are needed to improve diagnostic efficiency and quality. In this paper, a multi-label classification model combining attention-based neural networks and association-specific contexts is proposed for the detection of multiple lesions on chest X-ray images. A convolutional neural network and a long short-term memory network are first aligned by an attention mechanism to take advantage of both image and text information for the detection, called CNN-ATTENTION-LSTM (CAL) network. In addition, a mining method of implicit association strength to obtain an association network of chest lesions (CLA) network is designed to guide the training of CAL network. The CLA network provides possible clinical relationships between lesions to help the CAL network obtain better predictions. Experimental results on ChestX-ray14 dataset show that our method outperforms some state-of-the-art models under the metrics of area under curve (AUC), precision, recall, and F-score and achieves up to 85.4% in the case of atelectasis and infiltration. It indicates that the method may be useful in the computer-aided detection of multiple lesions on chest X-ray images.
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Therese, A. Brintha, and P. Bhuvaneswari Samuel. "Feature Extraction and Classification of COPD Chest X-ray Images." International Journal of Computer Aided Engineering and Technology 12, no. 6 (2020): 1. http://dx.doi.org/10.1504/ijcaet.2020.10010445.
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Bhuvaneswari, P., and A. Brintha Therese. "Feature extraction and classification of COPD chest X-ray images." International Journal of Computer Aided Engineering and Technology 12, no. 3 (2020): 301. http://dx.doi.org/10.1504/ijcaet.2020.106212.
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P, Allirani, Yogapriyaa S.P, Vishali M, Aezeden Mohamed, Akhmedov Abdulaziz, and S. Tharmar. "Classification of Chest X-ray Images using Convolutional Neural Nework." E3S Web of Conferences 399 (2023): 04048. http://dx.doi.org/10.1051/e3sconf/202339904048.
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The current worldwide Covid-19 epidemic is linked to a respiratory lung infection caused by a novel corona virus disease (SARSCoV- 2), the evolution of which is still not known. More than 100,000 cases were confirmed worldwide using the current case definition of Covid-19 infection, based on pneumonia diagnosis, with a death rate ranging between 2% and 3%. Since the expanding sick population might not have simple access to current laboratory testing, new screening techniques are necessary. The Computed tomography of chest is an important technique for the former detection and treatment of Covid-19 pulmonary symptoms, even though its utility as a screening tool has not yetbeen established. Even though it lacked specificity, it exhibited excellent sensitivity. We demonstrate a neural network based on pneumonia and covid classification in Tensor Flow and Keras. The suggested method is based on the CNN uses images and the CNN model to categorize Covid-19 or pneumonia. It is anticipated that discoveries will become more successful. If the covid-19 or pneumonia classification algorithms and other feature extraction methods are added, the CNN approach will be successfully supported.
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Costa, Nator Junior Carvalho da, Jose Vigno Moura Sousa, Domingos Bruno Sousa Santos, Francisco das Chagas Fontenele Marques Junior, and Rodrigo Teixeira de Melo. "Classification of x-ray images for detection of childhood pneumonia using pre-trained neural networks." Revista Brasileira de Computação Aplicada 12, no. 3 (2020): 132–41. http://dx.doi.org/10.5335/rbca.v12i3.10343.
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This paper describes a comparison between three pre-trained neural networks for the classification of chest X-ray images: Xception, Inception V3, and NasNetLarge. Networks were implemented using learning transfer; The database used was the chest x-ray data set, which contains a total of 5856 chest x-ray images of pediatric patients aged one to five years, with three classes: Normal Viral Pneumonia and Bacterial Pneumonia. Data were divided into three groups: validation, testing and training. A comparison was made with the work of kermany who implemented the Inception V3 network in two ways: (Pneumonia X Normal) and (Bacterial Pneumonia X Viral Pneumonia). The nets used had good accuracy, being the NasNetLarge network the best precision, which was 95.35 \% (Pneumonia X Normal) and 91.79 \% (Viral Pneumonia X Bacterial Pneumonia) against 92.80 \% in (Pneumonia X Normal) and 90.70 \% (Viral Pneumonia X Bacterial Pneumonia) from kermany's work, the Xception network also achieved an improvement in accuracy compared to kermany's work, with 93.59 \% at (Normal X Pneumonia) and 91.03 \% in (Viral Pneumonia X Bacterial Pneumonia).