# An Efficient Explainability of Deep Models on Medical Images
## 2 Related Work  
This section describes recent works on XAI. Authors [1] discuss the need to develop automatic COVID-19 detection systems to ease the workload of healthcare professionals in hospitals. COVID-19 is an epidemic that has affected almost every country in the world, causing enormous health, financial and emotional devastation, as well as the collapse of healthcare systems in some countries. Diagnosis of COVID-19, non-COVID-19 viral pneumonia and other lung opacities can be difficult on radiological images. The authors therefore use artificial intelligence to develop an automated detection system for COVID-19 from normal chest X-ray images. Using transfer learning, the authors ran three pre-trained models (Xception, VGG19 and ResNet50) on a reference dataset of 21,165 images. They first formulated the COVID-19 detection problem as a binary classification problem to classify COVID-19 against normal radiographic images. The results showed an accuracy of 97.5%, 97.5% and 93.3% for Xception, VGG19 and ResNet50, respectively. Next, they developed a multiclass classification model to differentiate COVID-19 from normal radiographic images, lung opacities and non-COVID-19 viral pneumonia. Results showed an accuracy of 75% for ResNet50, 92% for VGG19 and 93% for Xception. Although the performance of Xception and VGG19 were identical, Xception proved more effective with its higher precision, recall and F-1 scores. In addition, the authors employed GradCAM as an explainable AI on each model used, adding interpretability to the study. The results showed that Xception is more accurate at indicating the actual features that are responsible for a model's predictions. This addition of explainable AI can greatly help healthcare professionals by enabling them to visualize how a model makes its decision and not have to blindly trust the machine learning models developed. Paper [2] describes the use of machine learning for the detection of COVID-19 patients from CT scans and chest X-ray images. Authors used several pre-trained models to achieve this goal, including MobileNetV2, VGG16, InceptionV3, InceptionResNetV2, ResNet50, Xception, EfficientNetB0, EfficientNetB2 and EfficientNetB4. They created a dataset of 2753 CT scan and chest X-ray images from COVID-19 and healthy patients. They used the data augmentation technique to increase the size of the dataset and improve model performance. The results showed that the MobileNetV2-based model performed best in terms of precision (97.69%), recall (98.97%), F1 score (97.06%) and ROC curve (0.9926). The VGG16-based model also performed well, with precision of 96.61%, recall of 97.67%, F1 score of 96.1% and ROC curve of 0.9874. Models based on InceptionV3, InceptionResNetV2, ResNet50, Xception, EfficientNetB0, EfficientNetB2 and EfficientNetB4 also showed reasonable performance, with precision, recall and F1 scores above 90%. In addition, the authors used the LIME interpretability method to understand the most important features in the models' detection of COVID-19. The results showed that the areas important for COVID-19 detection were mainly the upper and lower zones of the lungs. This paper [3] presents a deep learning (DL)-based approach for the prediction and classification of chest X-ray images in different categories, including COVID-19, Pneumonia and Tuberculosis. The proposed DL model uses public chest X-ray data comprising 7132 images. To improve understanding and interpretation of the model results, the authors use explanatory AI techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM), Local Interpretable Model-agnostic Explanation (LIME) and SHapley Additive exPlanation (SHAP). These techniques visualize salient features and generate explanations for the decisions made by the DL model. The results obtained show an average accuracy of 94.31 \(\pm\) 1.01% for testing and 94.54 \(\pm\) 1.33% for validation, thanks to the use of the 10-fold cross-validation method.