# An Efficient Explainability of Deep Models on Medical Images
## 2 Related Work
In addition, the explanations generated by the explainable AI techniques have been validated by medical experts. The paper's conclusions highlight that the combination of explainable AI and DL models can deliver convincing and consistent results for the detection and classification of lung diseases. The proposed model features a lightweight architecture and superior performance compared with existing methods. However, the article also highlights some limitations of the study, including the fact that the model was trained on a small number of datasets and its performance on larger datasets was not tested. More sophisticated data augmentation approaches could be explored to improve the model's performance. In addition, the inclusion of patients' medical histories and other bodily symptoms in the data could contribute to better interpretation. In this recent paper [4], authors present an explainable artificial intelligence (AI) framework for interpreting lung diseases from chest X-rays. The main objective is to explain the classification results obtained for different lung diseases in order to help doctors understand the reasons that cause these diseases. The research used chest X-rays to classify different lung diseases such as edema, tuberculosis, nodules and pneumonia, including pneumonia caused by COVID-19. The proposed model is based on a transfer learning approach using the ResNet50 neural network, trained on COVID-CT and COVIDNet datasets of 800 images with two classes and 19,000 images with three classes, respectively. The model was fine-tuned and achieved improved classification results with an accuracy of 93% and 97%, respectively. To explain the classification results, the model uses LIME (Local Interpretable Model-agnostic Explanations) interpretability, which highlights the important features of the radiographic image that contributed to the classification of lung disease. Explanation results were evaluated by comparing the regions highlighted by the model with those identified by expert radiologists. The authors found that the model highlighted the same regions of ground-glass opacities as those identified by radiologists. The authors of this paper [5] focused on the detection of COVID-19, pneumonia and normal cases from chest X-ray images. They developed a deep learning algorithm based on a convolutional neural network (CNN) called DeepCCXR, which is an architecture based on EfficientNET-B5. The study used nine different datasets, comprising more than 3200 chest X-ray images of patients with COVID-19. The datasets used include images from the National Institute of Health (NIH) as well as other sources. The results obtained showed that the DeepCCXR model outperformed recent deep learning approaches for the detection of COVID-19 on chest X-ray images. The model achieved high Area Under Curve (AUC) scores with 0.973 for multiclass classification (COVID-19 vs. pneumonia vs. normal) and 0.986 for the binary model (COVID-19 vs. normal). Mean sensitivity and specificity were 0.97 and 0.98, respectively. For the COVID-19 class, sensitivity reached 0.99. The authors also developed an explainability algorithm that uses the Gradient-weighted Class Activation Mapping (Grad-CAM) technique to visualize the most important regions detected by the model. This enables infected areas in the lungs to be localized on chest X-ray images. Regions of dense, homogeneous opacity were identified as the most significant signs of COVID-19. These papers summarized above have discussed methods for detecting and classifying diseases from medical images using deep learning models. Although these works have achieved promising performances, it is important to discuss the limitations and shortcomings related to the explainability of the deep models used. In this section, we will examine some of these limitations and discuss the points to which the papers have not paid sufficient attention.