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].

翻译：本文开发了CT-xCOV，一种基于深度学习（DL）的CT扫描可解释COVID-19诊断框架。CT-xCOV采用从肺部分割到COVID-19检测及检测模型预测解释的端到端方法。在肺部分割中，我们采用了著名的U-Net模型。在COVID-19检测中，我们比较了三种不同的CNN架构：标准CNN、ResNet50和DenseNet121。检测完成后，提供视觉和文本解释。对于视觉解释，我们应用了三种不同的XAI技术，即Grad-Cam、积分梯度（IG）和LIME。通过计算肺部感染百分比添加文本解释。为评估所采用的XAI技术的性能，我们提出了一种基于真实标注的评估方法，测量可视化输出与真实感染区域之间的相似度。实验结果表明，所应用的深度学习模型取得了良好效果。U-Net分割模型实现了高Dice系数（98%）。通过5折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分数98.23%）。最后，XAI技术的比较结果表明，与LIME和IG相比，Grad-Cam提供了最佳解释：在COVID-19阳性扫描中，其Dice系数达到55%，而IG和LIME分别仅为29%和24%。本文使用的代码和数据集可在GitHub仓库[1]中获取。