Log in

Relevant bibliographies by topics / ResNet18 / Journal articles

To see the other types of publications on this topic, follow the link: ResNet18.

Journal articles on the topic 'ResNet18'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'ResNet18.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Cheah, Kit Hwa, Humaira Nisar, Vooi Voon Yap, Chen-Yi Lee, and G. R. Sinha. "Optimizing Residual Networks and VGG for Classification of EEG Signals: Identifying Ideal Channels for Emotion Recognition." Journal of Healthcare Engineering 2021 (March 30, 2021): 1–14. http://dx.doi.org/10.1155/2021/5599615.

Full text

Abstract:

Emotion is a crucial aspect of human health, and emotion recognition systems serve important roles in the development of neurofeedback applications. Most of the emotion recognition methods proposed in previous research take predefined EEG features as input to the classification algorithms. This paper investigates the less studied method of using plain EEG signals as the classifier input, with the residual networks (ResNet) as the classifier of interest. ResNet having excelled in the automated hierarchical feature extraction in raw data domains with vast number of samples (e.g., image processin

APA, Harvard, Vancouver, ISO, and other styles

2

Sanga, S. L., D. Machuve, and K. Jomanga. "Mobile-based Deep Learning Models for Banana Disease Detection." Engineering, Technology & Applied Science Research 10, no. 3 (2020): 5674–77. http://dx.doi.org/10.48084/etasr.3452.

Full text

Abstract:

In Tanzania, smallholder farmers contribute significantly to banana production and Kagera, Mbeya, and Arusha are among the leading regions. However, pests and diseases are a threat to food security. Early detection of banana diseases is important to identify the diseases before too much damage is done on the plants. In this paper, a tool for early detection of banana diseases by using a deep learning approach is proposed. Five deep learning architectures, namely Vgg16, Resnet18, Resnet50, Resnet152 and InceptionV3 were used to develop models for banana disease detection, achieving all high acc

APA, Harvard, Vancouver, ISO, and other styles

3

Kusumawardani, Rindi, and Putu Dana Karningsih. "Detection and Classification of Canned Packaging Defects Using Convolutional Neural Network." PROZIMA (Productivity, Optimization and Manufacturing System Engineering) 4, no. 1 (2021): 1–11. http://dx.doi.org/10.21070/prozima.v4i1.1280.

Full text

Abstract:

Packaging is one of the important aspects of a product’s identity. The good and adorable packaging can increase product competitiveness because it gives a perception to the customers of good quality products. Therefore, a good packaging display is necessary so that packaging quality inspection is very important. Automated defect detection can help to reduce human error in the inspection process. Convolutional Neural Network (CNN) is an approach that can be used to detect and classify a packaging condition. This paper presents an experiment that compares 5 network models, i.e. ShuffleNet, GoogL

APA, Harvard, Vancouver, ISO, and other styles

4

Setiawan, Wahyudi. "Klasifikasi Citra Histopatologi Kanker Payudara menggunakan Data Resampling Random dan Residual Network." JURNAL SISTEM INFORMASI BISNIS 11, no. 1 (2021): 70–77. http://dx.doi.org/10.21456/vol11iss1pp70-79.

Full text

Abstract:

Data imbalance between classes is one of the problems on image classification. The data in each class is not equal and has a relatively large difference generated in less than optimal classification results. Ideally, the data in each class is equal or have a slight difference. This article discusses the classification of the histopathology breast cancer image. The data consist of 8 classes with unbalanced data. The method for balancing the data in each class uses random resampling which is applied to training data only. The data used from BreakHist with magnifications of 40x, 100x, 200x, and 4

APA, Harvard, Vancouver, ISO, and other styles

5

Han, Lu, Chongchong Yu, Kaitai Xiao, and Xia Zhao. "A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification." Sensors 19, no. 9 (2019): 1960. http://dx.doi.org/10.3390/s19091960.

Full text

Abstract:

This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations. Therefore, the idea of mapping time series data into an analogous-image matrix data is p

APA, Harvard, Vancouver, ISO, and other styles

6

Mustamin, Nurul Fathanah, Yuslena Sari, and Husnul Khatimi. "KLASIFIKASI KUALITAS KAYU KELAPA MENGGUNAKAN ARSITEKTUR CNN." KLIK - KUMPULAN JURNAL ILMU KOMPUTER 8, no. 1 (2021): 49. http://dx.doi.org/10.20527/klik.v8i1.370.

Full text

Abstract:

<p><em>The increase in the export volume of coconut logs, which are materials that can efficiently substitute for conventional wood, demands that the quality of coconut wood classified quickly. However, due to the limitations of a grader as a human being, it is necessary to have assistance from machines or technology that can classify coconut wood quickly. Techniques that used for rapid classification can use computer visualization. Convolutional Neural Network (CNN) with the right architecture makes this method able to recognize and detect objects well, which influenced by compute

APA, Harvard, Vancouver, ISO, and other styles

7

Ahn, Jun Hyong, Heung Cheol Kim, Jong Kook Rhim, et al. "Multi-View Convolutional Neural Networks in Rupture Risk Assessment of Small, Unruptured Intracranial Aneurysms." Journal of Personalized Medicine 11, no. 4 (2021): 239. http://dx.doi.org/10.3390/jpm11040239.

Full text

Abstract:

Auto-detection of cerebral aneurysms via convolutional neural network (CNN) is being increasingly reported. However, few studies to date have accurately predicted the risk, but not the diagnosis itself. We developed a multi-view CNN for the prediction of rupture risk involving small unruptured intracranial aneurysms (UIAs) based on three-dimensional (3D) digital subtraction angiography (DSA). The performance of a multi-view CNN-ResNet50 in accurately predicting the rupture risk (high vs. non-high) of UIAs in the anterior circulation measuring less than 7 mm in size was compared with various CN

APA, Harvard, Vancouver, ISO, and other styles

8

Li, Xiali, Zhengyu Lv, Bo Liu, Licheng Wu, and Zheng Wang. "Improved Feature Learning: A Maximum-Average-Out Deep Neural Network for the Game Go." Mathematical Problems in Engineering 2020 (April 9, 2020): 1–6. http://dx.doi.org/10.1155/2020/1397948.

Full text

Abstract:

Computer game-playing programs based on deep reinforcement learning have surpassed the performance of even the best human players. However, the huge analysis space of such neural networks and their numerous parameters require extensive computing power. Hence, in this study, we aimed to increase the network learning efficiency by modifying the neural network structure, which should reduce the number of learning iterations and the required computing power. A convolutional neural network with a maximum-average-out (MAO) unit structure based on piecewise function thinking is proposed, through whic

APA, Harvard, Vancouver, ISO, and other styles

9

Sethy, Prabira Kumar, Santi Kumari Behera, Komma Anitha, Chanki Pandey, and M. R. Khan. "Computer aid screening of COVID-19 using X-ray and CT scan images: An inner comparison." Journal of X-Ray Science and Technology 29, no. 2 (2021): 197–210. http://dx.doi.org/10.3233/xst-200784.

Full text

Abstract:

The objective of this study is to conduct a critical analysis to investigate and compare a group of computer aid screening methods of COVID-19 using chest X-ray images and computed tomography (CT) images. The computer aid screening method includes deep feature extraction, transfer learning, and machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN models. The machine learning approach includes three sets of handcrafted features and three classifiers. The pre-trained CNN models include AlexNet, GoogleNet, VGG16, VG

APA, Harvard, Vancouver, ISO, and other styles

10

Yang, Cheng, Jingjie Chen, Zhiyuan Li, and Yi Huang. "Structural Crack Detection and Recognition Based on Deep Learning." Applied Sciences 11, no. 6 (2021): 2868. http://dx.doi.org/10.3390/app11062868.

Full text

Abstract:

The detection and recognition of surface cracks are of great significance for structural safety. This paper is based on a deep-learning methodology to detect and recognize structural cracks. First, a training dataset of the model is built. Then, three neural networks, AlexNet, VGGNet13, and ResNet18, are employed to recognize and classify crack images. The tests indicate that the ResNet18 model generates the most satisfactory results. It is also found that the trained YOLOv3 model detects the crack area with satisfactory accuracy. This study also confirms that the proposed deep learning as a n

APA, Harvard, Vancouver, ISO, and other styles

11

Yuan, Jian-ye, Xin-yuan Nan, Cheng-rong Li, and Le-le Sun. "Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network." Mathematical Problems in Engineering 2020 (November 20, 2020): 1–6. http://dx.doi.org/10.1155/2020/5795976.

Full text

Abstract:

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification mode

APA, Harvard, Vancouver, ISO, and other styles

12

Yu, Xiang, and Shui-Hua Wang. "Abnormality Diagnosis in Mammograms by Transfer Learning Based on ResNet18." Fundamenta Informaticae 168, no. 2-4 (2019): 219–30. http://dx.doi.org/10.3233/fi-2019-1829.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Teng, Shuai, Zongchao Liu, Gongfa Chen, and Li Cheng. "Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network." Applied Sciences 11, no. 2 (2021): 813. http://dx.doi.org/10.3390/app11020813.

Full text

Abstract:

This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO

APA, Harvard, Vancouver, ISO, and other styles

14

Koay, Hong Vin, Joon Huang Chuah, Chee-Onn Chow, Yang-Lang Chang, and Bhuvendhraa Rudrusamy. "Optimally-Weighted Image-Pose Approach (OWIPA) for Distracted Driver Detection and Classification." Sensors 21, no. 14 (2021): 4837. http://dx.doi.org/10.3390/s21144837.

Full text

Abstract:

Distracted driving is the prime factor of motor vehicle accidents. Current studies on distraction detection focus on improving distraction detection performance through various techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, the research on detection of distracted drivers through pose estimation is scarce. This work introduces an ensemble of ResNets, which is named Optimally-weighted Image-Pose Approach (OWIPA), to classify the distraction through original and pose estimation images. The pose estimation images are generated from HRNet a

APA, Harvard, Vancouver, ISO, and other styles

15

Jamali, Ali, Masoud Mahdianpari, Brian Brisco, Jean Granger, Fariba Mohammadimanesh, and Bahram Salehi. "Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery." Remote Sensing 13, no. 11 (2021): 2046. http://dx.doi.org/10.3390/rs13112046.

Full text

Abstract:

Wetlands are important ecosystems that are linked to climate change mitigation. As 25% of global wetlands are located in Canada, accurate and up-to-date wetland classification is of high importance, nationally and internationally. The advent of deep learning techniques has revolutionized the current use of machine learning algorithms to classify complex environments, specifically in remote sensing. In this paper, we explore the potential and possible limitations to be overcome regarding the use of ensemble deep learning techniques for complex wetland classification and discusses the potential

APA, Harvard, Vancouver, ISO, and other styles

16

Afifi, Ahmed, Abdulaziz Alhumam, and Amira Abdelwahab. "Convolutional Neural Network for Automatic Identification of Plant Diseases with Limited Data." Plants 10, no. 1 (2020): 28. http://dx.doi.org/10.3390/plants10010028.

Full text

Abstract:

Automated identification of plant diseases is very important for crop protection. Most automated approaches aim to build classification models based on leaf or fruit images. These approaches usually require the collection and annotation of many images, which is difficult and costly process especially in the case of new or rare diseases. Therefore, in this study, we developed and evaluated several methods for identifying plant diseases with little data. Convolutional Neural Networks (CNNs) are used due to their superior ability to transfer learning. Three CNN architectures (ResNet18, ResNet34,

APA, Harvard, Vancouver, ISO, and other styles

17

Liu, Yan, Guo-rong She, and Shu-xaing Chen. "Magnetic resonance image diagnosis of femoral head necrosis based on ResNet18 network." Computer Methods and Programs in Biomedicine 208 (September 2021): 106254. http://dx.doi.org/10.1016/j.cmpb.2021.106254.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Tuncer, Seda Arslan, Ahmet Çınar, and Murat Fırat. "Hybrid CNN Based Computer-Aided Diagnosis System for Choroidal Neovascularization, Diabetic Macular Edema, Drusen Disease Detection from OCT Images." Traitement du Signal 38, no. 3 (2021): 673–79. http://dx.doi.org/10.18280/ts.380314.

Full text

Abstract:

In the treatment of eye diseases, optical coherence tomography (OCT) is a medical imaging method that displays biological tissue layers by taking high resolution tomographic sections at the micron level. It has an important role in the diagnosis and follow-up of many diseases such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), age-related macular degeneration (AMD), Diabetic Retinopathy, Central Serous Retinopathy, Epiretinal Membrane, and Macular Hole. Computer-Aided Diagnostic (CAD) tools are needed in early detection and treatment monitoring of such eye diseases. In th

APA, Harvard, Vancouver, ISO, and other styles

19

Kuo, C. F., K. Zheng, S. Miao, et al. "OP0062 PREDICTIVE VALUE OF BONE TEXTURE FEATURES EXTRACTED BY DEEP LEARNING MODELS FOR THE DETECTION OF OSTEOARTHRITIS: DATA FROM THE OSTEOARTHRITIS INITIATIVE." Annals of the Rheumatic Diseases 79, Suppl 1 (2020): 41.2–42. http://dx.doi.org/10.1136/annrheumdis-2020-eular.2858.

Full text

Abstract:

Background:Osteoarthritis is a degenerative disorder characterized by radiographic features of asymmetric loss of joint space, subchondral sclerosis, and osteophyte formation. Conventional plain films are essential to detect structural changes in osteoarthritis. Recent evidence suggests that fractal- and entropy-based bone texture parameters may improve the prediction of radiographic osteoarthritis.1In contrast to the fixed texture features, deep learning models allow the comprehensive texture feature extraction and recognition relevant to osteoarthritis.Objectives:To assess the predictive val

APA, Harvard, Vancouver, ISO, and other styles

20

Sgibnev, I., A. Sorokin, B. Vishnyakov, and Y. Vizilter. "DEEP SEMANTIC SEGMENTATION FOR THE OFF-ROAD AUTONOMOUS DRIVING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 617–22. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-617-2020.

Full text

Abstract:

Abstract. This paper is devoted to the problem of image semantic segmentation for machine vision system of off-road autonomous robotic vehicle. Most modern convolutional neural networks require large computing resources that go beyond the capabilities of many robotic platforms. Therefore, the main drawback of such models is extremely high complexity of the convolutional neural network used, whereas tasks in real applications must be performed on devices with limited resources in real-time. This paper focuses on the practical application of modern lightweight architectures as applied to the tas

APA, Harvard, Vancouver, ISO, and other styles

21

Gao, Junbo, Yuanhao Guo, Yingxue Sun, and Guoqiang Qu. "Application of Deep Learning for Early Screening of Colorectal Precancerous Lesions under White Light Endoscopy." Computational and Mathematical Methods in Medicine 2020 (August 25, 2020): 1–8. http://dx.doi.org/10.1155/2020/8374317.

Full text

Abstract:

Background and Objective. Colorectal cancer (CRC) is a common gastrointestinal tumour with high morbidity and mortality. Endoscopic examination is an effective method for early detection of digestive system tumours. However, due to various reasons, missed diagnoses and misdiagnoses are common occurrences. Our goal is to use deep learning methods to establish colorectal lesion detection, positioning, and classification models based on white light endoscopic images and to design a computer-aided diagnosis (CAD) system to help physicians reduce the rate of missed diagnosis and improve the accurac

APA, Harvard, Vancouver, ISO, and other styles

22

Cha, So-Mi, Seung-Seok Lee, and Bonggyun Ko. "Attention-Based Transfer Learning for Efficient Pneumonia Detection in Chest X-ray Images." Applied Sciences 11, no. 3 (2021): 1242. http://dx.doi.org/10.3390/app11031242.

Full text

Abstract:

Pneumonia is a form of acute respiratory infection commonly caused by germs, viruses, and fungi, and can prove fatal at any age. Chest X-rays is the most common technique for diagnosing pneumonia. There have been several attempts to apply transfer learning based on a Convolutional Neural Network to build a stable model in computer-aided diagnosis. Recently, with the appearance of an attention mechanism that automatically focuses on the critical part of the image that is crucial for the diagnosis of disease, it is possible to increase the performance of previous models. The goal of this study i

APA, Harvard, Vancouver, ISO, and other styles

23

Aref, Nawres, and Hussain Kareem. "Detection of Covid-19 Based on Chest Medical Imaging and Artificial Intelligent Techniques: A Review." Iraqi Journal for Electrical and Electronic Engineering 17, no. 2 (2021): 176–82. http://dx.doi.org/10.37917/ijeee.17.2.19.

Full text

Abstract:

Novel Coronavirus (Covid-2019), which first appeared in December 2019 in the Chinese city of Wuhan. It is spreading rapidly in most parts of the world and becoming a global epidemic. It is devastating, affecting public health, daily life, and the global economy. According to the statistics of the World Health Organization on August 11, the number of cases of coronavirus (Covid-2019) reached nearly 17 million, and the number of infections globally distributed among most European countries and most countries of the Asian continent, and the number of deaths from the Coronavirus reached 700 thousa

APA, Harvard, Vancouver, ISO, and other styles

24

Li, Guoming, Xue Hui, Fei Lin, and Yang Zhao. "Developing and Evaluating Poultry Preening Behavior Detectors via Mask Region-Based Convolutional Neural Network." Animals 10, no. 10 (2020): 1762. http://dx.doi.org/10.3390/ani10101762.

Full text

Abstract:

There is a lack of precision tools for automated poultry preening monitoring. The objective of this study was to develop poultry preening behavior detectors using mask R-CNN. Thirty 38-week brown hens were kept in an experimental pen. A surveillance system was installed above the pen to record images for developing the behavior detectors. The results show that the mask R-CNN had 87.2 ± 1.0% MIOU, 85.1 ± 2.8% precision, 88.1 ± 3.1% recall, 95.8 ± 1.0% specificity, 94.2 ± 0.6% accuracy, 86.5 ± 1.3% F1 score, 84.3 ± 2.8% average precision and 380.1 ± 13.6 ms·image−1 processing speed. The six ResN

APA, Harvard, Vancouver, ISO, and other styles

25

Mohammed, Shivan H. M., and Ahmet Çinar. "Lung cancer classification with Convolutional Neural Network Architectures." Qubahan Academic Journal 1, no. 1 (2021): 33–39. http://dx.doi.org/10.48161/qaj.v1n1a33.

Full text

Abstract:

One of the most common malignant tumors in the world today is lung cancer, and it is the primary cause of death from cancer. With the continuous advancement of urbanization and industrialization, the problem of air pollution has become more and more serious. The best treatment period for lung cancer is the early stage. However, the early stage of lung cancer often does not have any clinical symptoms and is difficult to be found. In this paper, lung nodule classification has been performed; the data have used of CT image is SPIE AAPM-Lung. In recent years, deep learning (DL) was a popular appro

APA, Harvard, Vancouver, ISO, and other styles

26

Tasnim, Nusrat, Mohammad Khairul Islam, and Joong-Hwan Baek. "Deep Learning Based Human Activity Recognition Using Spatio-Temporal Image Formation of Skeleton Joints." Applied Sciences 11, no. 6 (2021): 2675. http://dx.doi.org/10.3390/app11062675.

Full text

Abstract:

Human activity recognition has become a significant research trend in the fields of computer vision, image processing, and human–machine or human–object interaction due to cost-effectiveness, time management, rehabilitation, and the pandemic of diseases. Over the past years, several methods published for human action recognition using RGB (red, green, and blue), depth, and skeleton datasets. Most of the methods introduced for action classification using skeleton datasets are constrained in some perspectives including features representation, complexity, and performance. However, there is still

APA, Harvard, Vancouver, ISO, and other styles

27

Li, Xiali, Zhengyu Lv, Licheng Wu, Yue Zhao, and Xiaona Xu. "Hybrid Online and Offline Reinforcement Learning for Tibetan Jiu Chess." Complexity 2020 (May 11, 2020): 1–11. http://dx.doi.org/10.1155/2020/4708075.

Full text

Abstract:

In this study, hybrid state-action-reward-state-action (SARSAλ) and Q-learning algorithms are applied to different stages of an upper confidence bound applied to tree search for Tibetan Jiu chess. Q-learning is also used to update all the nodes on the search path when each game ends. A learning strategy that uses SARSAλ and Q-learning algorithms combining domain knowledge for a feedback function for layout and battle stages is proposed. An improved deep neural network based on ResNet18 is used for self-play training. Experimental results show that hybrid online and offline reinforcement learni

APA, Harvard, Vancouver, ISO, and other styles

28

A. Al-Falluji, Ruaa, Zainab Dalaf Katheeth, and Bashar Alathari. "Automatic Detection of COVID-19 Using Chest X-Ray Images and Modified ResNet18-Based Convolution Neural Networks." Computers, Materials & Continua 66, no. 2 (2021): 1301–13. http://dx.doi.org/10.32604/cmc.2020.013232.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Shevelkin, Alexey V., Chantelle E. Terrillion, Yuto Hasegawa, et al. "Astrocyte DISC1 contributes to cognitive function in a brain region-dependent manner." Human Molecular Genetics 29, no. 17 (2020): 2936–50. http://dx.doi.org/10.1093/hmg/ddaa180.

Full text

Abstract:

Abstract Our understanding of the contribution of genetic risk factors to neuropsychiatric diseases is limited to abnormal neurodevelopment and neuronal dysfunction. Much less is known about the mechanisms whereby risk variants could affect the physiology of glial cells. Our prior studies have shown that a mutant (dominant-negative) form of a rare but highly penetrant psychiatric risk factor, Disrupted-In-Schizophrenia-1 (DISC1), impairs metabolic functions of astrocytes and leads to cognitive dysfunction. In order to overcome the limitations of the mutant DISC1 model and understand the putati

APA, Harvard, Vancouver, ISO, and other styles

30

Yamamoto, Norio, Shintaro Sukegawa, Kazutaka Yamashita, et al. "Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis." Medicina 57, no. 8 (2021): 846. http://dx.doi.org/10.3390/medicina57080846.

Full text

Abstract:

Background and Objectives: A few deep learning studies have reported that combining image features with patient variables enhanced identification accuracy compared with image-only models. However, previous studies have not statistically reported the additional effect of patient variables on the image-only models. This study aimed to statistically evaluate the osteoporosis identification ability of deep learning by combining hip radiographs with patient variables. Materials andMethods: We collected a dataset containing 1699 images from patients who underwent skeletal-bone-mineral density measur

APA, Harvard, Vancouver, ISO, and other styles

31

Bharati, Subrato, Prajoy Podder, M. Rubaiyat Hossain Mondal, and V. B. Surya Prasath. "CO-ResNet: Optimized ResNet model for COVID-19 diagnosis from X-ray images." International Journal of Hybrid Intelligent Systems 17, no. 1-2 (2021): 71–85. http://dx.doi.org/10.3233/his-210008.

Full text

Abstract:

This paper focuses on the application of deep learning (DL) based model in the analysis of novel coronavirus disease (COVID-19) from X-ray images. The novelty of this work is in the development of a new DL algorithm termed as optimized residual network (CO-ResNet) for COVID-19. The proposed CO-ResNet is developed by applying hyperparameter tuning to the conventional ResNet 101. CO-ResNet is applied to a novel dataset of 5,935 X-ray images retrieved from two publicly available datasets. By utilizing resizing, augmentation and normalization and testing different epochs our CO-ResNet was optimize

APA, Harvard, Vancouver, ISO, and other styles

32

Rao, Vishal, M. S. Priyanka, A. Lakshmi, et al. "Predicting COVID-19 pneumonia severity on chest X-ray with convolutional neural network: A retrospective study." Indian Journal of Medical Sciences 72 (December 31, 2020): 132–40. http://dx.doi.org/10.25259/ijms_349_2020.

Full text

Abstract:

Objectives: Radiological lung changes in COVID-19 infections present a noteworthy avenue to develop chest X-ray (CXR) -based testing models to support existing rapid detection techniques. The purpose of this study is to evaluate the accuracy of artificial intelligence (AI) -based screening model employing deep convolutional neural network for lung involvement. Material and Methods: An AI-based screening model was developed with state-of-the-art neural networks using Indian data sets from COVID-19 positive patients by authors of CAIR, DRDO, in collaboration with the other authors. Our dataset w

APA, Harvard, Vancouver, ISO, and other styles

33

Odusami, Modupe, Rytis Maskeliūnas, Robertas Damaševičius, and Tomas Krilavičius. "Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network." Diagnostics 11, no. 6 (2021): 1071. http://dx.doi.org/10.3390/diagnostics11061071.

Full text

Abstract:

One of the first signs of Alzheimer’s disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI), makes early detection of AD difficult. This paper proposes a deep learning-based method that can predict MCI, early MCI (EMCI), late MCI (LMCI), and AD. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) fMRI dataset consist

APA, Harvard, Vancouver, ISO, and other styles

34

Wang, Wei, Chengwen Zhang, Jinge Tian, et al. "High-Resolution Radar Target Recognition via Inception-Based VGG (IVGG) Networks." Computational Intelligence and Neuroscience 2020 (July 18, 2020): 1–11. http://dx.doi.org/10.1155/2020/8893419.

Full text

Abstract:

Aiming at high-resolution radar target recognition, new convolutional neural networks, namely, Inception-based VGG (IVGG) networks, are proposed to classify and recognize different targets in high range resolution profile (HRRP) and synthetic aperture radar (SAR) signals. The IVGG networks have been improved in two aspects. One is to adjust the connection mode of the full connection layer. The other is to introduce the Inception module into the visual geometry group (VGG) network to make the network structure more suik / for radar target recognition. After the Inception module, we also add a p

APA, Harvard, Vancouver, ISO, and other styles

35

Türkoğlu, Muammer. "Evrişimsel Sinir Ağları Kullanılarak Yumurta Kabuğu Kusurlarının Tespiti." Turkish Journal of Agriculture - Food Science and Technology 9, no. 3 (2021): 559–67. http://dx.doi.org/10.24925/turjaf.v9i3.559-567.4046.

Full text

Abstract:

In commercial egg farming industries, the automatic sorting of defective eggs is economically and healthily important. Nowadays, detect of defective eggs is performed manually. This situation involves time consuming, tiring and complex processes. For all these reasons, automatic classification of defects that may occur on the egg surface has become a very important issue. For this purpose, in this study, classification of egg defects was performed using AlexNet, VGG16, VGG19, SqueezeNet, GoogleNet, Inceptionv3, ResNet18, and Xception architectures, which were developed based on Convolutional N

APA, Harvard, Vancouver, ISO, and other styles

36

El Morabit, Safaa, Atika Rivenq, Mohammed-En-nadhir Zighem, Abdenour Hadid, Abdeldjalil Ouahabi, and Abdelmalik Taleb-Ahmed. "Automatic Pain Estimation from Facial Expressions: A Comparative Analysis Using Off-the-Shelf CNN Architectures." Electronics 10, no. 16 (2021): 1926. http://dx.doi.org/10.3390/electronics10161926.

Full text

Abstract:

Automatic pain recognition from facial expressions is a challenging problem that has attracted a significant attention from the research community. This article provides a comprehensive analysis on the topic by comparing some popular and Off-the-Shell CNN (Convolutional Neural Network) architectures, including MobileNet, GoogleNet, ResNeXt-50, ResNet18, and DenseNet-161. We use these networks in two distinct modes: stand alone mode or feature extractor mode. In stand alone mode, the models (i.e., the networks) are used for directly estimating the pain. In feature extractor mode, the “values” o

APA, Harvard, Vancouver, ISO, and other styles

37

Kazi Tani, L. F., A. Ghomari, and M. Y. Kazi Tani. "EVENTS RECOGNITION FOR A SEMI-AUTOMATIC ANNOTATION OF SOCCER VIDEOS: A STUDY BASED DEEP LEARNING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W16 (September 17, 2019): 135–41. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w16-135-2019.

Full text

Abstract:

<p><strong>Abstract.</strong> In this work, we propose an efficient way of web video annotation in soccer domain. To achieve this, it is necessary to enjoy different architectures of deep learning. We aim at realising a system of annotation able to recognise several events from information of the object that is the ball in our case, in order to fuse them as a part of actions in video. We propose to use Deep Neural Network (DNN) to detect ball and actions. However, Mask R-CNN can play a very important role for features extracted as an output using a training network on ImageNe

APA, Harvard, Vancouver, ISO, and other styles

38

Xia, Jingming, Dawei Xuan, Ling Tan, and Luping Xing. "ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural Network." Advances in Meteorology 2020 (May 26, 2020): 1–11. http://dx.doi.org/10.1155/2020/6972826.

Full text

Abstract:

Severe weather conditions will have a great impact on urban traffic. Automatic recognition of weather condition has important application value in traffic condition warning, automobile auxiliary driving, intelligent transportation system, and other aspects. With the rapid development of deep learning, deep convolutional neural networks (CNN) are used to recognize weather conditions on traffic road. A new simplified model named ResNet15 is proposed based on the residual network ResNet50 in this paper. The convolutional layers of ResNet15 are utilized to extract weather characteristics, and then

APA, Harvard, Vancouver, ISO, and other styles

39

Meghana, Avuthu Sai. "Age and Gender prediction using Convolution, ResNet50 and Inception ResNetV2." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 2 (2020): 1328–34. http://dx.doi.org/10.30534/ijatcse/2020/65922020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Li, Chang, Bangjin Yi, Peng Gao, et al. "Valuable Clues for DCNN-Based Landslide Detection from a Comparative Assessment in the Wenchuan Earthquake Area." Sensors 21, no. 15 (2021): 5191. http://dx.doi.org/10.3390/s21155191.

Full text

Abstract:

Landslide inventories could provide fundamental data for analyzing the causative factors and deformation mechanisms of landslide events. Considering that it is still hard to detect landslides automatically from remote sensing images, endeavors have been carried out to explore the potential of DCNNs on landslide detection, and obtained better performance than shallow machine learning methods. However, there is often confusion as to which structure, layer number, and sample size are better for a project. To fill this gap, this study conducted a comparative test on typical models for landside det

APA, Harvard, Vancouver, ISO, and other styles

41

Wang, Jinya, Zhenye Li, Qihang Chen, Kun Ding, Tingting Zhu, and Chao Ni. "Detection and Classification of Defective Hard Candies Based on Image Processing and Convolutional Neural Networks." Electronics 10, no. 16 (2021): 2017. http://dx.doi.org/10.3390/electronics10162017.

Full text

Abstract:

Defective hard candies are usually produced due to inadequate feeding or insufficient cooling during the candy production process. The human-based inspection strategy needs to be brought up to date with the rapid developments in the confectionery industry. In this paper, a detection and classification method for defective hard candies based on convolutional neural networks (CNNs) is proposed. First, the threshold_li method is used to distinguish between hard candy and background. Second, a segmentation algorithm based on concave point detection and ellipse fitting is used to split the adhesive

APA, Harvard, Vancouver, ISO, and other styles

42

Castro, Wellington, José Marcato Junior, Caio Polidoro, et al. "Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery." Sensors 20, no. 17 (2020): 4802. http://dx.doi.org/10.3390/s20174802.

Full text

Abstract:

Monitoring biomass of forages in experimental plots and livestock farms is a time-consuming, expensive, and biased task. Thus, non-destructive, accurate, precise, and quick phenotyping strategies for biomass yield are needed. To promote high-throughput phenotyping in forages, we propose and evaluate the use of deep learning-based methods and UAV (Unmanned Aerial Vehicle)-based RGB images to estimate the value of biomass yield by different genotypes of the forage grass species Panicum maximum Jacq. Experiments were conducted in the Brazilian Cerrado with 110 genotypes with three replications, t

APA, Harvard, Vancouver, ISO, and other styles

43

Tasnim, Nusrat, Md Mahbubul Islam, and Joong-Hwan Baek. "Deep Learning-Based Action Recognition Using 3D Skeleton Joints Information." Inventions 5, no. 3 (2020): 49. http://dx.doi.org/10.3390/inventions5030049.

Full text

Abstract:

Human action recognition has turned into one of the most attractive and demanding fields of research in computer vision and pattern recognition for facilitating easy, smart, and comfortable ways of human-machine interaction. With the witnessing of massive improvements to research in recent years, several methods have been suggested for the discrimination of different types of human actions using color, depth, inertial, and skeleton information. Despite having several action identification methods using different modalities, classifying human actions using skeleton joints information in 3-dimen

APA, Harvard, Vancouver, ISO, and other styles

44

Baskin, Chaim, Natan Liss, Eli Schwartz, et al. "UNIQ." ACM Transactions on Computer Systems 37, no. 1-4 (2021): 1–15. http://dx.doi.org/10.1145/3444943.

Full text

Abstract:

We present a novel method for neural network quantization. Our method, named UNIQ , emulates a non-uniform k -quantile quantizer and adapts the model to perform well with quantized weights by injecting noise to the weights at training time. As a by-product of injecting noise to weights, we find that activations can also be quantized to as low as 8-bit with only a minor accuracy degradation. Our non-uniform quantization approach provides a novel alternative to the existing uniform quantization techniques for neural networks. We further propose a novel complexity metric of number of bit operatio

APA, Harvard, Vancouver, ISO, and other styles

45

Pachón, César G., Dora M. Ballesteros, and Diego Renza. "Fake Banknote Recognition Using Deep Learning." Applied Sciences 11, no. 3 (2021): 1281. http://dx.doi.org/10.3390/app11031281.

Full text

Abstract:

Recently, some state-of-the-art works have used deep learning-based architectures, specifically convolutional neural networks (CNNs), for banknote recognition and counterfeit detection with promising results. However, it is not clear which design strategy is more appropriate (custom or by transfer learning) in terms of classifier performance and inference times for massive data applications. This paper presents a comparison of the two design strategies in various types of architecture. For the transfer learning (TL) strategy, the most appropriate freezing points in CNN architectures (sequentia

APA, Harvard, Vancouver, ISO, and other styles

46

Hong, Feng, Chengwei Liu, Lijuan Guo, Feng Chen, and Haihong Feng. "Underwater Acoustic Target Recognition with a Residual Network and the Optimized Feature Extraction Method." Applied Sciences 11, no. 4 (2021): 1442. http://dx.doi.org/10.3390/app11041442.

Full text

Abstract:

Underwater Acoustic Target Recognition (UATR) remains one of the most challenging tasks in underwater signal processing due to the lack of labeled data acquisition, the impact of the time-space varying intrinsic characteristics, and the interference from other noise sources. Although some deep learning methods have been proven to achieve state-of-the-art accuracy, the accuracy of the recognition task can be improved by designing a Residual Network and optimizing feature extraction. To give a more comprehensive representation of the underwater acoustic signal, we first propose the three-dimensi

APA, Harvard, Vancouver, ISO, and other styles

47

Werner, Julia, Raphael M. Kronberg, Pawel Stachura, et al. "Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2." Viruses 13, no. 4 (2021): 610. http://dx.doi.org/10.3390/v13040610.

Full text

Abstract:

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes COVID-19 and is responsible for the ongoing pandemic. Screening of potential antiviral drugs against SARS-CoV-2 depend on in vitro experiments, which are based on the quantification of the virus titer. Here, we used virus-induced cytopathic effects (CPE) in brightfield microscopy of SARS-CoV-2-infected monolayers to quantify the virus titer. Images were classified using deep transfer learning (DTL) that fine-tune the last layers of a pre-trained Resnet18 (ImageNet). To exclude toxic concentrations of potential drugs, the netwo

APA, Harvard, Vancouver, ISO, and other styles

48

Sun, Xinyan, Zhenye Li, Tingting Zhu, and Chao Ni. "Four-Dimension Deep Learning Method for Flower Quality Grading with Depth Information." Electronics 10, no. 19 (2021): 2353. http://dx.doi.org/10.3390/electronics10192353.

Full text

Abstract:

Grading the quality of fresh cut flowers is an important practice in the flower industry. Based on the flower maturing status, a classification method based on deep learning and depth information was proposed for the grading of flower quality. Firstly, the RGB image and the depth image of a flower bud were collected and transformed into fused RGBD information. Then, the RGBD information of a flower was set as inputs of a convolutional neural network to determine the flower bud maturing status. Four convolutional neural network models (VGG16, ResNet18, MobileNetV2, and InceptionV3) were adjuste

APA, Harvard, Vancouver, ISO, and other styles

49

Zhao, Wang, and Long Lu. "Research and development of autism diagnosis information system based on deep convolution neural network and facial expression data." Library Hi Tech 38, no. 4 (2020): 799–817. http://dx.doi.org/10.1108/lht-08-2019-0176.

Full text

Abstract:

PurposeFacial expression provides abundant information for social interaction, and the analysis and utilization of facial expression data are playing a huge driving role in all areas of society. Facial expression data can reflect people's mental state. In health care, the analysis and processing of facial expression data can promote the improvement of people's health. This paper introduces several important public facial expression databases and describes the process of facial expression recognition. The standard facial expression database FER2013 and CK+ were used as the main training samples

APA, Harvard, Vancouver, ISO, and other styles

50

Gao, Mingyu, Dawei Qi, Hongbo Mu, and Jianfeng Chen. "A Transfer Residual Neural Network Based on ResNet-34 for Detection of Wood Knot Defects." Forests 12, no. 2 (2021): 212. http://dx.doi.org/10.3390/f12020212.

Full text

Abstract:

In recent years, due to the shortage of timber resources, it has become necessary to reduce the excessive consumption of forest resources. Non-destructive testing technology can quickly find wood defects and effectively improve wood utilization. Deep learning has achieved significant results as one of the most commonly used methods in the detection of wood knots. However, compared with convolutional neural networks in other fields, the depth of deep learning models for the detection of wood knots is still very shallow. This is because the number of samples marked in the wood detection is too s

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!