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].

本文提出了一个可解释的COVID-19诊断框架CT-xCOV，采用深度学习（DL）在CT扫描中进行诊断。对于肺分割，使用了一种被广泛应用的U-Net模型。对于COVID-19检测，对比了三种不同的卷积神经网络架构：标准CNN、ResNet50和DenseNet121。在检测之后，提供了可视化和文本解释。通过计算肺部感染的百分比，添加了文本解释。通过对可视化输出和基于真实感染的标准进行相似度比较，提出了一种评估方法。实验结果表明，应用的DL模型效果良好。U-Net分割模型达到了较高的Dice系数（98%）。通过5倍交叉验证验证了我们提出的分类模型（标准CNN）的性能（准确率98.40％，f1得分98.23％）。最后，XAI技术的比较结果表明，与IG和LIME相比，Grad-Cam在COVID-19阳性扫描中达到55％的Dice系数，分别为29％和24％。本文所使用的代码和数据集可在GitHub存储库中获得。

CT-xCOV: 基于CT扫描的COVID-19诊断解释框架