Papers › Chest X-Ray Images Classification with CNN

Chest X-Ray Images Classification with CNN

31 Dec 2022International Marmara Sciences Congress 2022 12archive 2025-07-28

Ahmet Ekiz, Kaplan KAPLAN, Erdal Ayvaz

Chest radiography or called Chest X-ray(CXR) is common and one of the most cost-effective diagnostic procedures done in medical facilities. Interpretation of the chest X-ray is important in the diagnosis and detection of diseases such as pneumonia, pneumothorax, interstitial lung disease, heart failure, bone fracture, tuberculosis, pneumoconiosis, COVID-19, and even early lung cancer, as it contains a lot of information about the patient's medical condition. In practice, radiologists or consultants review these images and diagnose diseases, but this process can lead to misdiagnoses due to human error and expertise. Also, interpretation takes time. Assistive systems can be developed to reduce the impact of events that challenge health systems, such as the Covid-19 pandemic, to alleviate the workload and to reduce human error. For this purpose, chest X-ray images of a total of 336, Covid-19, Infiltrative and healthy patients collected from Derince Education and Research Hospital were collected. In this study, experiments were conducted to classify these images with Convolutional Neural Networks. Our CNN network with Residual Blocks and CNN with ResNet backbone by transfer learning were compared. As a result, the CNN model achieved 86.76% accuracy on test data, and (K=5) K-fold cross-validation 84.84% accuracy. The CNN with ResNet backbone by transfer learning achieved 89.71% accuracy on test data, and (K=5) K-fold cross-validation 84.23% accuracy.

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AhmetEkiz/chest_x_ray_images_classification_with_cnn tf report

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Classification Diagnostic Image Classification Medical Image Classification Transfer Learning

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Methods

1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Convolution Global Average Pooling Kaiming Initialization Max Pooling ReLU Residual Block Residual Connection Test

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