# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 4 Results and Discussion of Selected Articles
### Analysis and Discussion
Through activation mapping, we can verify the accuracy of the location in the model for classification or segmentation, assess its classification effectiveness, and compare whether its interpretability is truly feasible. [PERSON] [53] and colleagues proposed a new model for predicting oral squamous cell carcinoma (OSCC) tissue pathology images processed using deep transfer learning. Grad-CAM technology was employed for validation to pinpoint lesion areas in OSCC images based on the optimal model, significantly enhancing the prediction model's robustness. [PERSON] [18] and others used activation mapping technology for the performance evaluation of architectures. They introduced a deep learning-based coronary artery disease reporting and data system (CAD-RADS) for the classification of coronary artery lesions in multiplanar reconstructed images from coronary computed tomography angiography (CCTA). [PERSON] [23] and colleagues used bedside tasks such as the clock drawing test (CDT) to detect mild cognitive impairment (MCI) and dementia. They developed their deep learning framework, applied soft labels and self-attention to enhance model performance, and provided visual explanations. They utilized the Grad-CAM technique to evaluate their interpretable approach. However, it's important to note that since their classification subjects are not traditional medical images, their proposed interpretable methods may not necessarily be applicable in other domains. In addition to the two approaches mentioned above, the primary purpose of using CAM technology in most medical research is to achieve model interpretability, overcoming the black box issue in the model. [PERSON] and colleagues [48] conducted a study on the efficacy of CNNs in diagnosing Lyme disease from images. To enhance model interpretability, they applied Grad-CAM to highlight the key input regions essential for CNN predictions, thus making the model more robust. [PERSON] and colleagues [52] designed a multitask deep U-Net model that provides grade classification services for clinical glandular morphometric information and cancer. They employed Grad-CAM technology to provide further evidence of the self-learning capability of the model, and the visual results obtained were evaluated by expert pathologists. [PERSON] and [PERSON] [39] conducted research on stroke, building a CNN-based binary classifier to distinguish ECGs with stroke symptoms. They also leveraged Grad-CAM to aid model interpretation by illuminating subtle ECG patterns recognized by the model. In order to diagnose diabetic retinopathy (DR), [PERSON] and [PERSON] [44] developed a deep learning algorithm capable of detecting DR in retinal fundus images. They used an interpretable algorithm based on Grad-CAM to visually display the symbols chosen by the model, classifying retinal images for DR. This effectively demonstrated the model's interpretability, suggesting it can effectively pinpoint various indicators of DR and identify related health concerns. Innovative uses of activation mapping technology also exist. [PERSON] and colleagues [38] introduced a recurrent convolutional neural network model for intent recognition by learning decomposed spatiotemporal representations. They applied the Grad-CAM visualization technique for channel selection, achieving an impressive accuracy of 97.36% across all channels, surpassing many leading models and baselines. In summary, the application of activation mapping methods in healthcare primarily focuses on the following aspects:  
CAM technology serves as an explanatory tool, with its heatmap visualization providing an intuitive way to identify the regions of medical images that require the most attention. This not only helps in locating areas of pathology in medical images but also aids in observing these regions for model improvement. (1) CAM technology serves as a visualization tool, with its heatmap visualization providing an intuitive way to identify the regions of medical images that require the most attention.