  In this work, CT-xCOV, an explainable framework for COVID-19 diagnosis using
Deep Learning (DL) on CT-scans is developed. CT-xCOV adopts an end-to-end
approach from lung segmentation to COVID-19 detection and explanations of the
detection model's prediction. For lung segmentation, we used the well-known
U-Net model. For COVID-19 detection, we compared three different CNN
architectures: a standard CNN, ResNet50, and DenseNet121. After the detection,
visual and textual explanations are provided. For visual explanations, we
applied three different XAI techniques, namely, Grad-Cam, Integrated Gradient
(IG), and LIME. Textual explanations are added by computing the percentage of
infection by lungs. To assess the performance of the used XAI techniques, we
propose a ground-truth-based evaluation method, measuring the similarity
between the visualization outputs and the ground-truth infections. The
performed experiments show that the applied DL models achieved good results.
The U-Net segmentation model achieved a high Dice coefficient (98%). The
performance of our proposed classification model (standard CNN) was validated
using 5-fold cross-validation (acc of 98.40% and f1-score 98.23%). Lastly, the
results of the comparison of XAI techniques show that Grad-Cam gives the best
explanations compared to LIME and IG, by achieving a Dice coefficient of 55%,
on COVID-19 positive scans, compared to 29% and 24% obtained by IG and LIME
respectively. The code and the dataset used in this paper are available in the
GitHub repository [1].
