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Artykuły w czasopismach na temat „Lung Disease Detection”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Vidyasri S., Et al. "Automated Lung Disease Detection and Classification Using Quantum Glowworm Swarm Optimizer with Quasi Recurrent Neural Network on Chest X-Ray Images." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1150–59. http://dx.doi.org/10.17762/ijritcc.v11i10.8636.

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Lung diseases or otherwise called respiratory diseases are airborne diseases that affect the lungs and the other tissues of the lungs. Tuberculosis, Coronavirus Disease 2019 (COVID-19), and Pneumonia are a few instances of lung diseases. If the lung disease is diagnosed and treated in the initial stage, the chances of recovery rate and long-term survival rates can be increased. Usually, lung disease is identified by Chest X-Ray (CXR) image examination, skin test, sputum sample test, Computed Tomography (CT) scan examination, and blood test. Because of its non-invasive and convenient evaluation

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Tikendra, Sahu, and S. Choubey Aakanksha. "A review on lungs disease detection using image processing." i-manager's Journal on Information Technology 11, no. 1 (2022): 48. http://dx.doi.org/10.26634/jit.11.1.18536.

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The global fitness company estimates that by 2030, Chronic Obstructive Pulmonary Disease (COPD) will be the third leading cause of death in the world. Computerized Tomography (CT) of the lungs includes a number of structures that may be important in the prognosis and evaluation of lung disease. CT images of the lungs show a section of the chest that constitutes a large number of systems, including blood vessels, arteries, respiratory vessels, pulmonary pleura, and parenchyma, each with its own information. For this reason, the phasing of the lung systems is very important for the analysis and

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Acharya, Dipanjan, K. Eashwer, Soumya Kumar, R. Sivakumar, P. C. Kishoreraja, and Ramasamy Srinivasagan. "Multiple Disease Detection using Machine Learning Techniques." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 13 (2023): 120–37. http://dx.doi.org/10.3991/ijoe.v19i13.40523.

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The COVID-19 disease outbreak resulted in a worldwide pandemic. Currently, the reverse transcription-polymerase chain reaction (RT-PCR), which relies on nasopharyngeal swabs to examine the existence of the ribonucleic acid (RNA) of SARS-CoV-27, is still a popular approach to testing for the disease. Despite the high level of specificity of testing with RT-PCR, the sensitivity of the method could be relatively low, and there is significant variability in efficacy depending on different sampling methods and the time of occurrence of symptoms. It is therefore essential for us to develop a machine

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Chen, Taiyang. "Application of Machine Learning in Lung Disease Detection: A Review." Theoretical and Natural Science 89, no. 1 (2025): 188–93. https://doi.org/10.54254/2753-8818/2025.21453.

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Lung disease remains the leading cause of global mortality, which requires efficient and accurate methods for detection. Recent advancements in machine learning algorithms, artificial intelligence and image analysis technologies offer potential for early diagnosis and improved patient outcomes. This review summarizes different types of medical images that can be or have been frequently used in lung disease diagnosis, such as chest X-rays and CT scans. The paper then explores the role of key machine learning techniques in detecting various lung diseases, such as lung cancer, pneumonia and tuber

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Dubey, Sakshi. "Pulmonary Disease Prediction by Using Machine Learning Technique." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46795.

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Abstract - Pulmonary Disease is one of the leading causes of Cancer related deaths world wide and its early diagnosis and treatment are essential to cure the patient normally indicated by small growths in the lungs called nodules. It usually happens because cells in the lungs start increasing uncontrollably. Finding these Lung nodules is important for detecting lung Cancer, these nodules are typically detected through CT scans, but manual interpretation can be time-consuming and prone to human error. Through a process of feature extraction and selection, our model was trained to identify patte

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Prabu, S. Manikanda. "Automatic Detection of Lung Disease Using Machine Learning." Bonfring International Journal of Advances in Image Processing 14, no. 1 (2024): 8–10. https://doi.org/10.9756/bijaip/v14i1/bij24013.

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Medical image analysis is crucial for the early detection of lung illness, assisting doctors in providing appropriate therapies, and preventing fatalities. In this paper, an automated system is developed by fusing metaheuristic algorithm and machine learning classifier to differentiate between heathy lungs and affected lungs. The developed system has four phases such as preprocessing, feature extraction, feature selection, and classification. In the first phase, affected region is isolated from its background using thresholding method. The segmented image is used to construct the second phase'

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Sowmya, Veeramalla. "Lung Disease Detection Using CNN." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4042–49. http://dx.doi.org/10.22214/ijraset.2021.35595.

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Covid Pneumonia is a life-threatening bacterial disease in humans that affects one or both lungs and is caused by the bacteria Streptococcus pneumonia. Also known as Covid-19, this is a respiratory illness that was first discovered in Wuhan, China. Expert radiotherapists must evaluate chest X-rays used to diagnose pneumonia. As a result, establishing an autonomous system for detecting pneumonia would be advantageous for treating the condition quickly, especially in distant places. The statistical results show that using pre trained CNN models and supervised classifier algorithms to analyse che

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Hassan, Umaisa, Amit Singhal, and Priyanshu Chaudhary. "Lung disease detection using EasyNet." Biomedical Signal Processing and Control 91 (May 2024): 105944. http://dx.doi.org/10.1016/j.bspc.2024.105944.

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ANWAR, MR, MA BAKAR, HM AWAIS, et al. "EARLY DETECTION OF LUNGS CANCER USING MACHINE LEARNING ALGORITHMS." Biological and Clinical Sciences Research Journal 2023, no. 1 (2023): 187. http://dx.doi.org/10.54112/bcsrj.v2023i1.187.

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Medical healthcare systems store a large amount of clinical data about patients related to their biographies and disease information. Doctors use clinical data for the early detection of diseases that helps with proper patients’ treatments to save their lives. These clinical systems are helpful in detecting cancer diseases at early stages to save people's lives. Lung cancer is the third largely spreading disease in human beings all over the globe, which may lead so many people to death because of inaccurate detection of their disease at the initial stages. Therefore, this study will help docto

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Kasuga, Ikuma, Yoshimi Yokoe, Sanae Gamo, et al. "Which is a real valuable screening tool for lung cancer and measure thoracic diseases, chest radiography or low-dose computed tomography?: A review on the current status of Japan and other countries." Medicine 103, no. 19 (2024): e38161. http://dx.doi.org/10.1097/md.0000000000038161.

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Chest radiography (CR) has been used as a screening tool for lung cancer and the use of low-dose computed tomography (LDCT) is not recommended in Japan. We need to reconsider whether CR really contributes to the early detection of lung cancer. In addition, we have not well discussed about other major thoracic disease detection by CR and LDCT compared with lung cancer despite of its high frequency. We review the usefulness of CR and LDCT as veridical screening tools for lung cancer and other thoracic diseases. In the case of lung cancer, many studies showed that LDCT has capability of early det

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Choridah, Lina, Riries Rulaningtyas, Lailatul Muqmiroh, Suprayitno Suprayitno, and Khusnul Ain. "Detection of lung disease using relative reconstruction method in electrical impedance tomography system." Bulletin of Electrical Engineering and Informatics 12, no. 4 (2023): 2136–45. http://dx.doi.org/10.11591/beei.v12i4.4940.

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Lung disease can be diagnosed with the image-based medical devices, including radiography, computed tomography, and magnetic resonance imaging. The devices are very expensive and have negative effects. An alternative device is electrical impedance tomography (EIT). The advantages of EIT arelow cost, fast, real-time, and free radiation, so it is very appropriate to be used as a monitoring device. The relative reconstruction method has succeeded in producing functional images of lung anomalies by simulation. In this study, the relative reconstruction method was used to obtain functional images o

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Choridah, Lina, Riries Rulaningtyas, Lailatul Muqmiroh, Suprayitno Suprayitno, and Khusnul Ain. "Detection of lung disease using relative reconstruction method in electrical impedance tomography system." Bulletin of Electrical Engineering and Informatics 12, no. 4 (2023): 2136–45. http://dx.doi.org/10.11591/eei.v12i4.4940.

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Lung disease can be diagnosed with the image-based medical devices, including radiography, computed tomography, and magnetic resonance imaging. The devices are very expensive and have negative effects. An alternative device is electrical impedance tomography (EIT). The advantages of EIT arelow cost, fast, real-time, and free radiation, so it is very appropriate to be used as a monitoring device. The relative reconstruction method has succeeded in producing functional images of lung anomalies by simulation. In this study, the relative reconstruction method was used to obtain functional images o

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M, Albertini. "Canine Scent Detection of Lung Cancer: Preliminary Results." Open Access Journal of Veterinary Science & Research 1, no. 4 (2016): 1–5. http://dx.doi.org/10.23880/oajvsr-16000118.

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Several researches have evidenced that cancer cells can produce volatile organic compounds (VOCs) which are released not only in breath but also in other organic fluids, such as blood and urine. This study has evaluated the olfactory capability of trained dogs to detect human lung cancer VOCs in urine. We recruited 150 subjects from European Institute of Oncology (IEO) divided into three groups: 57 patients with lung cancer (group 1); 38 patients with lung disease, other than cancer (group 2); 55 healthy co ntrol subjects (group 3).The results are referred to the last 45 days of training, and

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Kwon, Hyuk-Ju, and Sung-Hak Lee. "A Two-Step Learning Model for the Diagnosis of Coronavirus Disease-19 Based on Chest X-ray Images with 3D Rotational Augmentation." Applied Sciences 12, no. 17 (2022): 8668. http://dx.doi.org/10.3390/app12178668.

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Herein, we propose a method for effectively classifying normal, coronavirus disease-19 (COVID-19), lung opacity, and viral pneumonia symptoms using chest X-ray images. The proposed method comprises a lung detection model, three-dimensional (3D) rotational augmentation, and a two-step learning model. The lung detection model is used to detect the position of the lungs in X-ray images. The lung position detected by the lung detection model is used as the bounding box coordinates of the two-step learning model. The 3D rotational augmentation, which is a data augmentation method based on 3D photo

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Sri, Widodo, Nur Rohmah Ratnasari, Handaga Bana, and Dyah Dewi Arini Liss. "Lung diseases detection caused by smoking using support vector machine." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 3 (2019): 1256–66. https://doi.org/10.12928/TELKOMNIKA.v17i3. 9799.

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Type of lung disease is very much manifold, but type of lung disease caused by smoking there are only 4, namely Bronchitis, Pneumonia, Emphysema and Lung Cancer. Doctors usually diagnose lung disease from CT scans using the naked eye, then interpret data one by one.This procedure is not effective. The aim of this research is improvement accuracy of lung diseases detection caused by smoking using support vector machine on computed tomography scan (CT scan) images. This study includes 4 (four) main points. First is the development of software for segmentation of lung organ automatically using Ac

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Kousiga, T., and P. Nithya. "Multi-Scale Multi-Class Generative Adversarial Network for Improving Deep Learner based Lung Disease Detection." Indian Journal Of Science And Technology 18, no. 14 (2025): 1105–17. https://doi.org/10.17485/ijst/v18i14.93.

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Objective: The prediction results of Deep Learning (DL) model rely heavily on labeled training images and the quality of images in the datasets. The most of datasets consists of blurry and low quality images due to reconstruction and compressions processes. The main objective of this research is to generate quality and sufficient images for improving lung disease prediction of CovLscan. Methods: Radiopaedia-Chest X-Ray and Kaggle-COVID-19 Lung CT datasets are used for lung disease prediction. Multi-Scale Multi-Class Generative Adversarial Network (MsMcGAN) model is proposed for generating qual

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Kalra, Sanjay, Brian Bartholmai, and Xiaoming Zhang. "Application of lung ultrasound surface wave elastography in the evaluation of diffuse lung diseases." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A221. http://dx.doi.org/10.1121/10.0023342.

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The evaluation of diffuse lung disease invariably involves chest computerized tomography (CT). The detailed information obtained is often equivalent to that from lung biopsies. It, however, involves exposure to ionising radiation which limits its repeated application in monitoring and follow up. Ultrasound techniques offer potentially safer/cheaper alternatives and lung ultrasound surface wave elastography (LUSWE) is a novel, noninvasive, and clinically feasible technique to quantify lung stiffness, an important biomechanical property that changes in diffuse fibrotic and infiltrative lung dise

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Amit, Kore, Kandekar Saurabh, Yadav Anurag, and Ohol Shubham. "Detection of Lung Cancer Disease using Machine Learning." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 810–12. https://doi.org/10.35940/ijeat.C6128.069520.

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Cancer causes cell to split uncontrollably. Lung Cancer results in rapid cell growth and division of such infected cell, such growth of cells called tumor. Lung is the first organ where lung tumor begins and can spread to lymph nodes and so on. Early identification of lung cancer would facilitate in sparing a large no. of lives. If we compare death rates in any cancer then lung cancer has highest mortally rate. This article presents an automated webbased system for disease detection in lung using X-Ray images. To identify disease in lung in X-ray images, as it provides detailed picture and giv

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Kieu, Stefanus Tao Hwa, Abdullah Bade, Mohd Hanafi Ahmad Hijazi, and Hoshang Kolivand. "A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions." Journal of Imaging 6, no. 12 (2020): 131. http://dx.doi.org/10.3390/jimaging6120131.

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The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to prese

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Mangaraj, Rojalin. "Lung Disease Detection Using Machine Learning Algorithms." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51287.

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Lung disease including pneumonia, tuberculosis, COPD, and COVID-19remain major public health challenges. Early, accurate diagnosis from chest X-rays or CT scans is crucial, yet manual interpretation is time-consuming and susceptible to human error. This study presents a comparative deep learning framework leveraging three architecturesVGG16, ResNet18, and Vision Transformer (ViT) to detect and classify lung disease from medical imaging. We curated a dataset of 3,475 chest X-ray images labelled into three classes: normal, lung opacity, and pneumonia. Data preprocessing included resizing, normal

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.SaiTeja, P., M. .Kalyan, and P. Bhuvan Kumar. "A Survey on Lung Disease Diagnosis using Machine Learning Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43887.

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Respiratory illnesses continue to seriously impact global health. Since the 2020 outbreak, death rates for those with lung conditions have alarmingly climbed. Early diagnoses prove crucial to promptly treating patients and bettering outcomes. Traditional testing frequently necessitates too much time before results, delaying vital interventions.Advances in computer vision, deep learning algorithms, and openly accessible datasets now allow integrating artificial intelligence in medical diagnoses. Machine learning models have dramatically cut detection times and lessened manual review in lung dis

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Jiang, Wenfa, Ganhua Zeng, Shuo Wang, Xiaofeng Wu, and Chenyang Xu. "Application of Deep Learning in Lung Cancer Imaging Diagnosis." Journal of Healthcare Engineering 2022 (January 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/6107940.

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Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pollution, and population aging, the cancer burden of lung cancer is increasing day by day. In the diagnosis of lung cancer, Computed Tomography (CT) images are a fairly common visualization tool. CT images visualize all tissues based on the absorption of X-rays. The diseased parts of the lung are collectively referred to as pulmonary nodules, the sha

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Felsen, Csilla N., Elamprakash N. Savariar, Michael Whitney, and Roger Y. Tsien. "Detection and monitoring of localized matrix metalloproteinase upregulation in a murine model of asthma." American Journal of Physiology-Lung Cellular and Molecular Physiology 306, no. 8 (2014): L764—L774. http://dx.doi.org/10.1152/ajplung.00371.2013.

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Extracellular proteases including matrix metalloproteinases (MMPs) are speculated to play a significant role in chronic lung diseases, such as asthma. Although increased protease expression has been correlated with lung pathogenesis, the relationship between localized enzyme activity and disease progression remains poorly understood. We report the application of MMP-2/9 activatable cell-penetrating peptides (ACPPs) and their ratiometric analogs (RACPPs) for in vivo measurement of protease activity and distribution in the lungs of mice that were challenged with the allergen ovalbumin. MMP-2/9 a

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Sasi, Smitha, and Srividya B. V. "Prediction and Estimation of Lung Cancer and Authenticating by CNN-ECC Model." International Journal of Organizational and Collective Intelligence 11, no. 3 (2021): 14–37. http://dx.doi.org/10.4018/ijoci.2021070102.

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Miscellany of data analysis on the genesis of disease and the outcome of mortality is very crucial to keep track of the death rates induced due to the disease. The primary detection of the presence of viral infections in lungs is one of the major concerns in the health industry in today's scenario. These infections can lead to mortality. Therefore, the classification and analysis of disease are very pivotal along with security of data. Hence, it is essential for detecting diseases using CNN algorithm at an early stage and generation of medical report automatically. The method is tested for dif

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Narayanan, Vidyul, Nithya P., and Sathya M. "Effective lung cancer detection using deep learning network." Journal of Cognitive Human-Computer Interaction 5, no. 2 (2023): 15–23. http://dx.doi.org/10.54216/jchci.050202.

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The use of a computer-assisted diagnosis system was crucial to the results of the clinical study conducted to determine the nature of the human illness. When compared to other disorders, lung cancer requires extra caution during the examination process. This is because the mortality rate from lung cancer is higher because it affects both men and women. Poor image resolution has hampered previous lung cancer detection technologies, preventing them from achieving the requisite degree of dependability. Therefore, in this study, we provide a unique approach to lung cancer prognosis that makes use

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Y, Naveen,. "Brain Tumor and Lung Disease 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/ijsrem32674.

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Brain Tumor and Lung Disease Detection using Deep Learning stands at the forefront of medical innovation, revolutionizing diagnostics with cutting-edge technology. Leveraging intricate algorithms and convolutional neural networks (CNNs), it automates the analysis of MRI scans for brain tumors and chest CT-Scans for lung diseases. Through meticulous feature extraction, this system precisely identifies diverse tumor types like glioma, meningioma, and pituitary tumors, along with lung pathologies such as bacterial pneumonia, viral pneumonia, and tuberculosis. By harnessing the power of deep learn

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Tiple, Aman, and Shlok Kadam. "Deep Learning Approach for Lung Disease Detection by Image Processing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–4. http://dx.doi.org/10.55041/ijsrem37406.

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In this research, the detection of lung diseases is targeted for ILD (interstitial lung disease) emphysema consolidation and fibrosis by image processing approaches. High resolution CT scans of the lung are analyzed using a combination of several techniques that include deep learning models such as convolutional neural networks (CNNs), image segmentation, feature extraction and classification methods. CNNs improve the accuracy of a variety of manual image feature extraction methods for disease pattern recognition. In this study, transfer learning with pre-trained models is used to improve the

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Hasanah, N., F. Arifin, D. Irmawati, and Muslikhin. "Smart System for Lung Disease Early Detection." Journal of Physics: Conference Series 1140 (December 2018): 012035. http://dx.doi.org/10.1088/1742-6596/1140/1/012035.

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Gupta, Naman, Deepak Gupta, Ashish Khanna, Pedro P. Rebouças Filho, and Victor Hugo C. de Albuquerque. "Evolutionary algorithms for automatic lung disease detection." Measurement 140 (July 2019): 590–608. http://dx.doi.org/10.1016/j.measurement.2019.02.042.

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Ariatna Alia, Putri. "LITERATURE REVIEW: LUNG DISEASE DETECTION BASED ON X-RAY USING ARTIFICIAL INTELLIGENCE." Jurnal Rekayasa Sistem Informasi dan Teknologi 1, no. 2 (2023): 50–53. http://dx.doi.org/10.59407/jrsit.v1i2.152.

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According to a WHO survey in 2019, 4 of the 10 most common diseases that kill people are lung disease. Lung disease is a significant problem for all of us, but until now there has not been found an effective drug to detect it earlier, so that in general lung disease is diagnosed in a severe condition. One example of a lung disease taken as a sample is pneumonia. This research aims to develop a method that is faster and more accurate in detecting individuals infected with pneumonia by using Artificial Intelligence, especially by using Convolutional Neural Network (CNN) architecture in its learn

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Reddy, D. Narsimha, C. Ganesh, K. Shiva Abhigna, and P. Tharun Sai. "Lung Cancer Detection Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 2172–78. http://dx.doi.org/10.22214/ijraset.2024.59257.

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Abstract: Lung cancer ranks among the primary causes of death on global scale. Catching this disease early can increase your chances or opportunities of survival. Computer-assisted detection (CAD) is used to create CT images and even X-rays of the lungs to determine whether cancer is present in the images. This paper represents an image classification by the combination of a neural network (CNN) algorithm and support vector machine (SVM). The algorithm spontaneously separates and analyzes lung picture or image to detect cancer cells. Compared to full-scale networks, CNNs are easier to train an

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Akulkina, L. A., M. Yu Brovko, V. I. Sholomova, et al. "Variety of lung involvement in autoimmune liver diseases." Terapevticheskii arkhiv 90, no. 8 (2018): 107–12. http://dx.doi.org/10.26442/terarkh2018908107-112.

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The primary autoimmune liver diseases conventionally include primary biliary cholangitis, primary sclerosing cholangitis and autoimmune hepatitis. Despite of primary autoimmune affection of different parts of the hepatobiliary system, in the recent decades, a lot of data has emerged indicating the presence of extrahepatic manifestations of these diseases, in particular, lung lesions, such as nodular and interstitial changes with possible progression and development of fibrosis and respiratory failure. In case of lungs disease, both pulmonary parenchyma and lung vessels, pleura, and intrathorac

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Vohra, Bhavna, and Sumit Mittal. "Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review." Journal of Experimental Biology and Agricultural Sciences 11, no. 2 (2023): 226–35. http://dx.doi.org/10.18006/2023.11(2).226.235.

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Diagnosis of lung diseases like asthma, chronic obstructive pulmonary disease, tuberculosis, cancer, etc., by clinicians rely on images taken through various means like X-ray and MRI. Deep Learning (DL) paradigm has magnified growth in the medical image field in current years. With the advancement of DL, lung diseases in medical images can be efficiently identified and classified. For example, DL can detect lung cancer with an accuracy of 99.49% in supervised models and 95.3% in unsupervised models. The deep learning models can extract unattended features that can be effortlessly combined into

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Yu, Yang, Yiyang Li, Chengbi Tong, Xiaomeng Zheng, and Chao Yang. "The Clinical Value of Detecting the Level of Exfoliated Cells in Pleural Effusion by Flow Cytometry in the Differential Diagnosis of Non-Small Cell Lung Cancer and Benign Lung Diseases." Journal of Clinical and Nursing Research 7, no. 3 (2023): 165–69. http://dx.doi.org/10.26689/jcnr.v7i3.4849.

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Objective: To explore the value of flow cytometry (FCM) in detecting the level of exfoliated cells in pleural effusion in the differential diagnosis of non-small cell lung cancer and benign lung diseases. Methods: Clinical data of patients with non-small cell lung cancer who were hospitalized in Hebei hospital from June 2019 to March 2022 were collected. A total of 98 patients were included, and 63 patients with alveolar lung disease were screened during the same period, and the two groups of patients were analyzed. Results: Compared with alveolar lung disease group, FCM detection and analysis

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Gulzira, Abdikerimova, Shekerbek Ainur, Tulenbayev Murat, et al. "Detection of chest pathologies using autocorrelation functions." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 4526–34. https://doi.org/10.11591/ijece.v13i4.pp4526-4534.

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An important feature of image analysis is texture, seen in all images, from aerial and satellite images to microscopic images in biomedical research. A chest X-ray is the most common and effective method for diagnosing severe lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the largest X-ray object. The correct separation of the shapes and sizes of the contours of the lungs is an important reason for diagnosis, because of which an intelligent information environment can be created. Despite the use of X-rays, to identify the diagnosis, there is a chance that the disease

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Nafea, Ahmed Adil, Mohammed Salah Ibrahim, Mustafa Muslih Shwaysh, Kibriya Abdul-Kadhim, Hiba Rashid Almamoori, and Mohammed M. AL-Ani. "A Deep Learning Algorithm for Lung Cancer Detection Using EfficientNet-B3." Wasit Journal of Computer and Mathematics Science 2, no. 4 (2023): 68–76. http://dx.doi.org/10.31185/wjcms.209.

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Lung carcinoma is one of the main causes of deaths over the whole world, causing a global burden of morbidity and mortality. Detecting lung tumors at their early stages can help reducing the risk of having lung cancer. This paper proposes a deep learning algorithm using EfficientNet B3 for lung cancer detection. The purpose is to improve detection accuracy highlighting potential to revolutionize the field of medical imaging and improve patient care. The proposed approach is build based on EfficientNet B3 model to classify four different types of lung cancer. The approach used CT scan images la

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Narayana, Budati Jaya Lakshmi, Gopireddy Krishna Teja Reddy, Sujana Sri Kosaraju, and Sirigiri Rajeev Choudhary. "INTEGRATED HYBRID MODEL FOR LUNG DISEASE DETECTION THROUGH DEEP LEARNING." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 14, no. 3 (2024): 81–85. http://dx.doi.org/10.35784/iapgos.6081.

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The burden of lung diseases on world health is substantial, underscoring the vital necessity of timely detection. The VGG16 architecture with additional convolutional layers is used in this study to provide a hybrid method to lung disease classification. It incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to improve model performance in response to the problem of imbalanced class instances. The subset of the NIH Chest X-ray dataset is used to train and assess the model. The designed model classifies the images into 8 different classes of lung diseases. They are Emphysema, Ca

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Prabhu, R., S. Sathya, P. Umaeswari, and K. Saranya. "Lung cancer disease identification using hybrid models." Scientific Temper 14, no. 03 (2023): 821–26. http://dx.doi.org/10.58414/scientifictemper.2023.14.3.40.

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Using hybrid models, we present a novel method for detecting lung cancer in this study. Our method uses the random forest and convolutional neural network (CNN) techniques to incorporate machine learning and deep learning advantages. The proposed composite method combines structured clinical data with unprocessed imaging data for a more complete lung cancer diagnosis. The CNN component of our hybrid model excels at extracting features from images of lung cancer, while the random forest component excels at capturing complex relationships in structured data. For greater precision and consistency

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Kapnadak, Siddhartha G., and Ganesh Raghu. "Lung transplantation for interstitial lung disease." European Respiratory Review 30, no. 161 (2021): 210017. http://dx.doi.org/10.1183/16000617.0017-2021.

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Lung transplantation (LTx) can be a life-extending treatment option for patients with advanced and/or progressive fibrotic interstitial lung disease (ILD), especially idiopathic pulmonary fibrosis (IPF), fibrotic hypersensitivity pneumonitis, sarcoidosis and connective tissue disease-associated ILD. IPF is now the most common indication for LTx worldwide. Several unique features in patients with ILD can impact optimal timing of referral or listing for LTx, pre- or post-transplant risks, candidacy and post-transplant management. As the epidemiology of LTx and community practices have evolved, r

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Nayak, Seema, Shivam Kumar, Archit Aggarwal, and Nikhil Kumar. "Detection of microparticles through the LoRa module." Spectrum of Emerging Sciences 3, no. 1 (2023): 39–43. http://dx.doi.org/10.55878/ses2023-3-1-9.

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Particles in the PM2.5 size range can enter the respiratory system and go to the lungs. Short-term health impacts from exposure to these microscopic particles include coughing, sneezing, runny nose, and breathing difficulties, as well as irritation of the eyes, nose, throat, and lungs. The lungs can be harmed by exposure to these minute particles, which can also raise the risk of heart disease and asthma. According to scientific studies, daily PM2.5 exposure increases are linked to an increase in mortality as well as cardiovascular and respiratory problems. Studies have shown that exposure to

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Shitole, Shubham. "Respiratory Diseases Detection using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2698–701. http://dx.doi.org/10.22214/ijraset.2021.35507.

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Prediction of the Respiratory diseases in the earlier stage can be very useful specially to improve the survival rate of that patient. CT scan images are used to detect various lung diseases .These CT scan reports are sent to pathologists for further process. Pathologists analyze CT scan report and predict the infected tissues which are the main cause of the particular disease. This is lengthy process and to avoid this steps and increase the accuracy of the prediction Machine learning plays an important role . The system proposes to build "Predictive Diagnostic System" of infectious lung by us

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Piwko, Adam, Amelia Kosior-Romanowska, and Justyna Chałdaś - Majdańska. "Detection and analysis of disease entities based on lung conditions." Journal of Modern Science 57, no. 3 (2024): 580–93. http://dx.doi.org/10.13166/jms/191301.

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The article presents a method for detecting and analysing disease entities associated with lung diseases. The results are related to work on the design of a medical diagnostic system based on impedance tomography. One of the key features of the solution is its ability to diagnose respiratory diseases, particularly chronic obstructive pulmonary disease (COPD), acute respiratory distress syndrome (ARDS) and pneumothorax (PTX). The article describes the results of a classification model that effectively distinguishes between healthy and sick patients, achieving an impressive accuracy of 99.86%. T

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Iqbal, Hammad, Arshad Khan, Narayan Nepal, Faheem Khan, and Yeon-Kug Moon. "Deep Learning Approaches for Chest Radiograph Interpretation: A Systematic Review." Electronics 13, no. 23 (2024): 4688. http://dx.doi.org/10.3390/electronics13234688.

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Lung diseases are a major global health concern, with nearly 4 million deaths annually, according to the World Health Organization (WHO). Chest X-rays (CXR) are widely used as a cost-effective and efficient diagnostic tool by radiologists to detect conditions such as pneumonia, tuberculosis, COVID-19, and lung cancer. This review paper provides an overview of the current research on diagnosing lung diseases using CXR images and Artificial Intelligence (AI), without focusing on any specific disease. It examines different approaches employed by researchers to leverage CXR, an accessible diagnost

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Liu, Yang, Li Nan Fan, and Shen Shen Sun. "Computer Aided Detection Method of Lung Fissure Based on CT Images." Advanced Materials Research 466-467 (February 2012): 596–601. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.596.

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CT images of Lung Fissure detection is an important means of centrifuged lung disease. In recent years has emerged many new theories, new methods, but none of those methods can satisfy all symptoms of image accurate testing goal. In summary of domestic and international scholars in recent years related researches on and the detecting lung fissure method classification and discussion, and points out their respective advantages and problems. In this paper, the relevant methods also expounds each detection lung fissure method based on the features of other researchers in lung fissure methods sele

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Mubashir Ali. "Lung Cancer Detection using Supervised Machine Learning Techniques." Lahore Garrison University Research Journal of Computer Science and Information Technology 6, no. 1 (2022): 49–68. http://dx.doi.org/10.54692/lgurjcsit.2022.0601276.

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In recent times, Lung cancer is the most common cause of mortality in both men and women around the world. Lung cancer is the second most well-known disease after heart disease. Although lung cancer prevention is impossible, early detection of lung cancer can effectively treat lung cancer at an early stage. The possibility of a patient's survival rate increasing if lung cancer is identified early. To detect and diagnose lung cancer in its early stages, a variety of data analysis and machine learning techniques have been applied. In this paper, we applied supervised machine learning algorithms

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Zhou, Changhao. "Lung diseases classification based on auto-machine learning." Applied and Computational Engineering 18, no. 1 (2023): 182–87. http://dx.doi.org/10.54254/2755-2721/18/20230988.

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The escalating incidence of lung diseases and the substantial costs associated with their diagnosis underscore the significance of developing approaches that can enhance the effectiveness and precision of detection. In this regard, this study employs an auto-machine learning platform called Edge Impulse to train a greyscale dataset of lung diseases. Specifically, the study compares two classification models and two transfer learning models generated by edge impulse and BrainChip, respectively, and employs accuracy as a metric to assess the efficacy of these models. The research analyzes the po

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Benaya, Dony. "Implementasi Random Forest dalam Klasifikasi Kanker Paru-Paru." JOINTER : Journal of Informatics Engineering 5, no. 01 (2024): 27–31. http://dx.doi.org/10.53682/jointer.v5i01.331.

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Lung cancer is a serious disease that threatens global health due to its high morbidity and mortality rates. To reduce lung cancer mortality, a more precise diagnostic approach is needed. The aim of this study was to improve the sensitivity and specificity in detecting lung cancer, to support early detection efforts and more effective management. The research method involved a series of steps from data collection to model performance evaluation. Data was collected, cleaned, and analyzed for correlation before going through the preprocessing stage. Model training was conducted using the Random

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Abdikerimova, Gulzira, Ainur Shekerbek, Murat Tulenbayev, et al. "Detection of chest pathologies using autocorrelation functions." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 4526. http://dx.doi.org/10.11591/ijece.v13i4.pp4526-4534.

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<p><span lang="EN-US">An important feature of image analysis is texture, seen in all images, from aerial and satellite images to microscopic images in biomedical research. A chest X-ray is the most common and effective method for diagnosing severe lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the largest X-ray object. The correct separation of the shapes and sizes of the contours of the lungs is an important reason for diagnosis, because of which an intelligent information environment can be created. Despite the use of X-rays, to identify the diagnosis, t

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Sousa Júnior, Pedro Cavalcante, Luís Fabrício de Freitas Souza, José Jerovane da Costa Nascimento, et al. "Detection and Segmentation of Lungs Regions Using CNN Combined with Levelset." Learning and Nonlinear Models 19, no. 1 (2021): 45–54. http://dx.doi.org/10.21528/lnlm-vol19-no1-art4.

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Lung diseases are among the leaders in ranking diseases that kill the most globally. A quick and accurate diagnosis made by a specialist doctor facilitates the treatment of the disease and can save lives. In recent decades, an area that has gained strength in computing has been the aid to medical diagnosis. Several techniques were created to help health professionals in their work using Computer Vision Techniques and Machine Learning. This work presents a method of lung segmentation based on deep learning and computer vision techniques to aid in the medical diagnosis of lung diseases. The meth

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Shusuke, Masaomi Takizawa, Sone Shodayu Takashima, Li Feng Yuichiro, Maruyama Minoru, Hasegawa Kazuhisa, and Hanamura Kazuhiro Asakura. "The Mobile Hospital- an experimental telemedicine system for the early detection of disease." Journal of Telemedicine and Telecare 4, no. 3 (1998): 146–51. http://dx.doi.org/10.1258/1357633981932127.

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To detect early disease, especially lung disease, we created a new telemedicine system, called the Mobile Hospital. It consists of a satellite or ground communication system using a large vehicle in which is installed a whole-body spiral computerized tomography scanner and a multimedia telecommunications system. The Mobile Hospital goes to small villages and carries out mass screening for early lung disease, especially lung cancer. It travelled over 8000 km from May 1996 to March 1997. In this period 6358 persons aged 50-85 years and younger heavy smokers were screened in the Matsumoto area. A

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