Số tạp chí Ngoc Thanh Nguyen · Bogdan Franczyk · André Ludwig · Manuel Núñez · Jan Treur · Gottfried Vossen · Adrianna Kozierkiewicz(2024) Trang: 157-169
Lungs are highly susceptible to attacks from various agents around us, and we often suffer from diseases that can be life-threatening. This study presents a diagnosis approach based on a combination of the well-known convolutional neural network architecture, VGG-16, and model interpretation techniques such as Grad-CAM and LIME. This approach helps visualize the lung areas infected with COVID-19 and other considered anomalies such as Pleural thickening and Pulmonary fibrosis. Also, it utilizes model-explanation techniques to visualize lung lesion areas. Also, we have attempted to provide explanations of predic- tion via all layers of VGG-16 by Grad-CAM and investigated the number of superpixels with LIME. The experimental results evaluated on Com- puted Tomography (CT) images collected from COVID-19 patients and healthy lungs reveal the promising combination between the image classi- fication of VGG-16 and interpretation methods of Grad-CAM and LIME in lung disease diagnosis.
Tạp chí khoa học Trường Đại học Cần Thơ
Lầu 4, Nhà Điều Hành, Khu II, đường 3/2, P. Xuân Khánh, Q. Ninh Kiều, TP. Cần Thơ
Điện thoại: (0292) 3 872 157; Email: tapchidhct@ctu.edu.vn
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