Interpreting Results of VGG-16 for COVID-19 Diagnosis on CT Images

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 prediction via all layers of VGG-16 by Grad-CAM and investigated the number of superpixels with LIME. The experimental results evaluated on Computed Tomography (CT) images collected from COVID-19 patients and healthy lungs reveal the promising combination between the image classification of VGG-16 and interpretation methods of Grad-CAM and LIME in lung disease diagnosis.