## 4 Dataset Description
### RoI Localization and Segmentation  
The performance of deep learning is noteworthy. Nevertheless, a shortcoming of deep learning architectures is that they are considered as black boxes which do not provide any insight into the classification process. Therefore, in this phase the activation maps are generated by Norm-VGG16 for our classes to visualize and localize the areas used for classification. The activation maps highlight the focus areas, from which most of the CNN-based features were extracted.  
In order to generate discriminative localization map, Gradient-weighted Class Activation Mapping (Grad-CAM) module [37] is used. The implemented Grad-CAM module structure is shown in Figure 6. Grad-CAM \(L^{c}{}_{Grad-CAM}\in R^{i\omega c}\) of width \(u\) and height \(v\) for any class \(c\). The gradient of the score of the class \(c\) is computed before the SoftMax layer (\(y_{c}\)) with respect to feature maps \(A_{k}\) of a convolutional layer \(\frac{\partial\varphi}{\partial A^{k}}\). These gradients flowing back are global average pooled to obtain the neuron importance weights \(\alpha^{c}_{k}\) as shown in Equation (5).  
\[\alpha^{c}_{k}= \tag{5}\]  
Figure 6: Grad-CAM overview.  
This weight \(\alpha_{c}{}^{k}\) represents a partial linearization of the deep network downstream from \(A\) and captures the \"importance\" of feature map \(k\) for a target class \(c\). After that a weighted combination of forward activation maps takes place. RELU is applied on the combination of feature maps. RELU is used to emphasize the positive influencer pixels whose intensity should be increased in order to increase \(y^{c}\) for a certain class and remove the negative influencer pixels that are likely to belong to other categories in the image as shown in Equation (6).  
\[L_{Grad-CAM}^{c}=ReLU\underbrace{\left(\sum_{k}\alpha_{k}^{c}\,A^{k}\right)}_{ linear\ combination} \tag{6}\]  
The RELU helps in highlighting the positive influencer pixels in the localization maps and avoid highlighting the negative influencer pixels, which achieve better localization [37].  
The generated heat maps (activation maps) seem to clearly portray the RoIs used in classification, which can be considered as implicit segmentation. Thus, they provide transparency of the model, which is required in medical diagnosis. The generated heat maps for all the four classes are shown in Figure 7, where the activation maps clearly mark the RoI.  
The shown samples of the activation maps clearly mark the RoI that can be used in classification and feature generation. As shown in Figure 8, a binary mask is generated for each image from the RoI and the corresponding image is segmented using this binary mask.  
The binary masks are generated based on the heat maps output by the Norm-VGG16 deep learning architecture. The heat maps highlight the RoI, which allow the generation of binary masks. Simple global thresholding [38; 39] is used, where a histogram is plotted for each heat map and the respective threshold value is determined. The threshold value is selected per individual heat map such that red and yellow areas in the heat map are used to create the mask (emphasizing the localized RoI region) as shown in Figure 9. The global threshold value is used to generate the binary mask.  
Figure 7: Examples of heat maps for each class.