# The Usefulness of Gradient-Weighted CAM in Assisting Medical Diagnoses
## 1 Introduction
The Grad-CAM method provides a visual form of explanation for the results of CNN models via the computation of heat-maps. It does so through the backpropagation of the result to the last convolution layer in the CNN model and weighting the gradient information to determine the importance of each neuron concerning the input image. It then generates a heat-map showing the importance of each region, as shown below in Figure 2. In Figure 2, \(w_{k}^{c}\) are the weights, where \(c\) represents the classification class and \(k\) the numbering of the feature map extracted from the CNN after the classification. If \(A\) represents the feature map, and \(Z\) is the total number of features in each \(A\), then  
\[w_{k}^{c}=Z\ \frac{\partial Y^{c}}{\partial A_{i,j}^{k}},\ \text{where}\ \Upsilon^{c}\ \text{is gradient of score for class}\ c \tag{1}\]  
Grad-CAM can be applied to the following families of CNN models: (1) CNNs with fully connected layers, (2) CNNs for structured outputs, and (3) CNNs with multimodal inputs or reinforcement learning tasks without any architectural changes or retraining. Combining Grad-CAM heat-maps with fine-graininess and suitability for visualization, it is possible to achieve the conditions for creating high-resolution visualizations in multiple categories and applying them to ready-made image classification. In the context of image classification models, the benefits are: (1) a deep understanding of their failure modes, (2) robustness to adversarial images, (3) superiority to previously mentioned CNN-explanatory methods in terms of localization, (4) greater faithfulness to the training results of the underlying model, and (5) assistance in achieving goals by identifying data-set biases. There have been many interesting investigations into the applicability of Grad-CAM in various CNN-based applications. In [17], a CNN-based AI structure was used to classify patients' races based on medical images alone, and the investigation not only examined the classification accuracy but also the underlying mechanism for the results of the classifications using Grad-CAM. The results presented in this paper found that the CNN-based AI could distinguish between races based on the medical images alone with an accuracy \begin{table}
\begin{tabular}{c c} \hline \hline
**Paper Reference** & **Summary** \\ \hline \multirow{4}{*}{[14]} & Compared the heat-maps generated using CAM, Grad-CAM, and Grad-CAM++ using 3 types of multiple sclerosis MRI images using the best-performing CNN models in terms of classification accuracy and concluded that Grad-CAM shows the best heat-map localizing ability. \\ \hline \multirow{4}{*}{[15]} & Compared the heat-maps generated by Saliency Map, Grad-CAM, and Grad-CAM++ using chest X-ray images. Found that Grad-CAM generally localized pathologies better than the other methods, but slightly worse than the human benchmark. \\ \hline \multirow{4}{*}{[16]} & Evaluated the performance of Grad-CAM on breast mammogram images, and showed that Grad-CAM has good localization capability after the pectoral muscle was removed from the images. Concluded that for improving diagnosis, classification accuracy as well as obtaining a reasonable heat-map is important. \\ \cline{1-1}  & \\ \cline{1-1}  & \\ \end{tabular}
\end{table} 
Table 1: Summary of related papers. around the 90 th percentile, which is better than most doctors. The underlying mechanisms for such determinations were not as expected, based on the results of the heat-maps from Grad-CAM. This interesting investigation shows that Grad-CAM may provide explanations for decisions made by CNN-based AI methods that are beyond human expectations. Because the comparisons of the effectiveness of Grad-CAM against other CNN-explanatory methods have already been made in the literature, as mentioned before, it is not the purpose of this paper to reinvestigate this aspect nor to compare the characteristics of Grad-CAM with the other methods. Instead, this investigation seeks to probe the explanatory power of Grad-CAM in physician-centered diagnoses.