Algorithm for lung pathology detection in X-ray images using binary classification with emphasis on preprocessing

O b j e c t i v e s. The purpose of the work is automatic detection of lung lesions: cavities, infiltrates, and nodules on chest X-ray images. Also, the possibility of spatial localization of these lesions on the image is investigated. M e t h o d s. Binary classification using deep convolutional neural networks and the Grad-CAM method are used. Re s u lt s. For the Xception model, the binary classification accuracy on the test dataset is 73.1% for cavities, 71.9% for infiltrates, and 72.8% for nodules. Heat maps with true positive outcomes for cavities and nodules are mostly understandable to radiologists. More research is needed to get heat maps for infiltrates that are understandable to experts. Co n c l u s i o n. The average classification accuracy of the Xception model for three lesion types (cavities, infiltrates, and nodules) is equal to 72.6%. Heat maps associated with pathological processes in the lungs and lesion localization were constructed. Obtained results are good, but not excellent. Thus, further investigation should be done to improve the classification accuracy and quality of the heat maps.