{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/chest-x-ray-images-classification-with-cnn","title":"Chest X-Ray Images Classification with CNN","arxiv_id":null,"date":"2022-12-31","proceeding":"International Marmara Sciences Congress 2022 12","authors":["Ahmet Ekiz","Kaplan KAPLAN","Erdal Ayvaz"],"abstract":"Chest radiography or called Chest X-ray(CXR) is common and one of the most cost-effective diagnostic\r\nprocedures done in medical facilities. Interpretation of the chest X-ray is important in the diagnosis and\r\ndetection of diseases such as pneumonia, pneumothorax, interstitial lung disease, heart failure, bone\r\nfracture, tuberculosis, pneumoconiosis, COVID-19, and even early lung cancer, as it contains a lot of\r\ninformation about the patient's medical condition. In practice, radiologists or consultants review these\r\nimages and diagnose diseases, but this process can lead to misdiagnoses due to human error and\r\nexpertise. Also, interpretation takes time. Assistive systems can be developed to reduce the impact of\r\nevents that challenge health systems, such as the Covid-19 pandemic, to alleviate the workload and to\r\nreduce human error. For this purpose, chest X-ray images of a total of 336, Covid-19, Infiltrative and\r\nhealthy patients collected from Derince Education and Research Hospital were collected. In this study,\r\nexperiments were conducted to classify these images with Convolutional Neural Networks. Our CNN\r\nnetwork with Residual Blocks and CNN with ResNet backbone by transfer learning were compared. As\r\na result, the CNN model achieved 86.76% accuracy on test data, and (K=5) K-fold cross-validation\r\n84.84% accuracy. The CNN with ResNet backbone by transfer learning achieved 89.71% accuracy on\r\ntest data, and (K=5) K-fold cross-validation 84.23% accuracy.","url_abs":"https://www.researchgate.net/publication/367254188_Chest_X-Ray_Images_Classification_with_CNN","url_pdf":"https://www.researchgate.net/profile/Kaplan-Kaplan/publication/367254188_Chest_X-Ray_Images_Classification_with_CNN/links/63c91775e922c50e99a80386/Chest-X-Ray-Images-Classification-with-CNN.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"chest-x-ray-images-classification-with-cnn","repo_url":"https://github.com/AhmetEkiz/chest_x_ray_images_classification_with_cnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"medical-image-classification","task_name":"Medical Image Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}