# The Usefulness of Gradient-Weighted CAM in Assisting Medical Diagnoses
## 1 Introduction  
In cases where physicians are required to make a clinical diagnosis based on medical images derived from technologies such as magnetic resonance imaging (MRI), or computed tomography (CT) scans, even a minor error in judgment may result in adverse effects or complications for the patient. Many applications of deep learning methodology in the detection and classification of abnormalities in pre-operative medical images have been proposed recently in the literature [1, 2, 3]. In many of these studies, methods based on the convolutional neural network (CNN) structure have shown great promise in their abilities in prediction and classification [4, 5]. A CNN-based architecture is composed of convolution layers to help extract features and build feature maps from the input image(s), pooling layers to concentrate these features, and fully connected layers to classify or predict theresult using the features computed in the previous layers [6]. These modules of the CNN are shown below in Figure 1.  
Variations of the basic CNN structure can be constructed by altering the numbers and sizes of these layers. Once constructed, the feature maps can then be extracted from the convolution layers for computational purposes and can also be visualized, if necessary. However, because most variations of the CNN architectures are composed of large numbers of these different layers, this produces incredibly high numbers of possible features due to variations in the combinations of different types of layers. Though these features contribute to the effectiveness of the CNN networks in terms of accuracy, they are disadvantageous in trying to explain the reason for the final results due to their large numbers. This lack of explainability is one of the main reasons for the lack of trust in medical results derived from AI-based systems [7]. If explanations for the AI's decisions could be presented to the physicians or radiologists during diagnosis, and are deemed to be helpful, then it may be possible to increase the rate of acceptance for AI-assisted diagnoses, and possibly reduce the number of false diagnoses by physicians. Several approaches have been developed in the literature in the hope of providing possible explanations for the results. Among these, the most promising approaches seek to provide some form of visual heat-maps with higher intensity values around the input image regions containing important features and information that the CNN network used in determining its results [8,9]. These promising methods include class-activation mapping (CAM) [10], saliency map [11], and a modified CAM called the gradient-weighted CAM (Grad-CAM) [12], and its variation, the Grad-CAM++ [13], which was proposed by a different author. In terms of performance, comparisons between the methods mentioned above, in the applications for medical images, have already been made in the literature regarding the characteristics of the heat-maps they generate [14, 15, 16]. A quick summary of the content of these papers is shown below in Table 1.  
Figure 1: Components of a CNN deep learning network. These papers first found the best CNN-based classifiers for their chosen medical images, then generated the heat-maps using the above-mentioned methods, and compared the characteristics of the different heat-maps. These papers concluded that Grad-CAM shows the best performance in terms of localization, which is a desired property for heat-maps; i.e., a more localized heat-map is better at showing the separation of boundaries of the locations containing features that contributed the most toward the classification results, and thus may provide better discrimination and possible explanations for the decision(s) made by the CNN. It is important to note that these heat-maps are not the feature maps of the convolution layers; rather, they show the hierarchy of importance of locations within the feature maps that contributes to the final classification result.