Recently, Artificial Intelligence (AI) and more particularly Deep Learning (DL) applications gained significant importance in several domains such as computer vision, robotics, medical imaging, etc. Despite the excellent results of AI models, in terms of precision and performance, their decisions are not always interpretable and explainable, which makes from them a black box. Since May 2018, the general data protection regulation (GDPR) requires a right of explanation for the output of an algorithm, which is necessary and justified for several examples such as autonomous cars and computer-aided diagnosis (CAD) systems. As a result, a high interest in terms of research has been given recently to the domain of Explainable Artificial Intelligence (XAI). In this book chapter, we propose an approach for explaining Deep Learning algorithms when applied to image classification and segmentation. The proposed approach allows to provide the most appropriate explanation method and the most accurate and explainable DL model. As a use case, we applied our approach for explaining DL models used Covid-19 image classification and segmentation with two modalities: X-ray and CT-scan images. Experimental results showed the interest of our explanation approach within three facts: (1) identification of the most interpretable DL model;(2) measurement of positive and negative contributions of input parameters (image pixels) in the decision of DL models;(3) detection of data (training and validation datasets) biases, where the deep neural networks are focusing on image regions that are not supposed to be important. The provided explanations were evaluated by doctors and physicians who confirmed the accuracy of our results. © 2022, Springer Nature Switzerland AG.
Explainable Deep Learning for Covid-19 Detection Using Chest X-ray and CT-Scan Images