## 3 Proposed Approach
### Feature Extraction/Classification
#### 3.2.1 CAM Methodology  
The methodology introduced by [PERSON] et al. [12] encompasses the proposition of class activation mapping (CAM). CAM serves to identify discriminative regions within images, offering insights into the specific features that neural networks prioritize when making decisions. This technique facilitates a nuanced interpretation of the visual elements neural networks consider during decision-making processes, thereby contributing to an enhanced understanding of the underlying mechanisms governing neural network decisions. Figure 5 visually presents CAMs for multiple images across distinct classes. The network architecture comprises several convolutional layers, culminating in global average pooling (GAP) immediately preceding the final output layer. Extracted features are subsequently conveyed to an FC layer, incorporating softmax activation to yield the requisite output. Evaluation of the significance of specific image regions involves the projection of output layer weights onto the convolutional feature maps generated by the preceding layer. The mathematical expressions defining CAM are delineated as follows: let \(f_{k}(x,y)\) denote the activation map of unit \(k\) in the final convolutional layer at spatial coordinates \((x,y)\). The outcome of the GAP, denoted by \(F_{k}\), is expressed as:  
\[F_{k}=\frac{1}{(x\times y)}\sum_{i=1}^{x}\sum_{j=1}^{y}f_{k}(x,y) \tag{3}\]  
For a class, \(c\), the input, \(S_{c}\), to the softmax function is given by:  
\[S_{c}=\sum_{k=1}^{K}W_{k}(c)\ F_{k} \tag{4}\]  
where \(W_{k}(c)\) represents the weight corresponding to class \(c\) for unit \(k\), indicating the significance of \(F_{k}\) for class \(c\), and \(K\) denotes the total number of activation maps in the final convolutional layer. Figure 4: A graphical flowchart of the proposed Mean-CAM-CNN framework. The output, \(P_{c}\), of the softmax layer can be expressed as:  
\[P_{c}=\frac{\exp(S_{c})}{\sum_{k=1}^{K}\exp(S_{c})} \tag{5}\]  
where \(c\ \in\ \{1,2,...,C\}\), \(C\) represents the total number of classes, and \(S_{c}\) represents the elements of the input vector to the softmax function. Thus, the final equation for the activation map of class \(c\) would be \(H_{c}(x,y)\):  
\[H_{c}(x,y)=\sum_{k=1}^{K}W_{k}(c)f_{k}(x,y) \tag{6}\]  
where \(H_{c}(x,y)\) denotes the significance of activation at the spatial grid \((x,y)\), leading to the image's classification into class \(c\). Here, \(k\in\{1,2,...,K\}\) and \(K\) represent the number of activation maps in the last convolutional layer. \(W_{k}(c)\) represents the weight associated with class \(c\) for unit \(k\), while \(f_{k}(x,y)\) refers to the activation map of unit \(k\). CAMs are constructed by projecting the expected class score back onto the previous convolutional layer. This emphasizes the unique areas that are relevant to each class. The process of CAM is illustrated in Figure 6. As previously stated, the GAP layer is preferred over the global max-pooling (GMP) layer due to its ability to determine the full extent of the object. Conversely, GMP layers only identify a discriminative component. With GAP, the activation is averaged across all areas, providing insight into all discriminative regions, while the GMP layer only analyzes the most discriminative one. The GAP layer computes the average value for each feature map of the final convolutional layer, which is multiplied by a weight and added together to generate the final output. Likewise, the class activation map is generated by computing the weighted sum of the final convolutional layer. Figure 5: CAMs for various images belonging to different classes from the AWE and MAI datasets. The maps display the pertinent image regions utilized for image classification. We chose ResNet-50 for our study because it has a top layer that is compatible with the CAM technique. The top layer of ResNet-50 is a GAP layer, followed by a fully connected layer that has a softmax activation function.