## 7 Discussion
### Analysis of Interpretability and Limitations of the Proposed Model  
With the interpretability analysis of neural networks, it is possible to have a visual explanation of the decisions made by the so-called \"black-box\". For such analysis, the Grad-CAM algorithm is used in the present work. This algorithm uses the gradient information (global mean) flowing into the last convolutional layer of the CNN to assign importance values (weights) to each neuron for a particular decision of interest [77]. The convolutional layers naturally retain spatial information that is lost in the fully connected layers; therefore, the last convolutional layers can be expected to have the best trade-off between high-level semantics and detailed spatial information. Neurons in these layers search for class-specific semantic information in the image (e.g., parts of objects). The heat map of the image that results from applying this algorithm highlights the features of the image. The Grad-CAM algorithm is applied on the last convolutional layer of the proposed custom CNN, that is, before the injection of texture features. Some results obtained are shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author and Year** & **Accuracy** & **Sensitivity** & **Precision** & **Specificity** & **AUC** \\ \hline [PERSON] et al. [76] 2017 & 76.9 & 97.40 & - & 48.40 & - \\ \hline Proposed work & 81.50 & 92.71 & 74.79 & 71.15 & 81.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of proposed work for the classification of melanoma vs. nevus with existing work on DermIS database. All values are in percentages. The symbol “-” indicates missing information. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author and Year** & **Accuracy** & **Sensitivity** & **Precision** & **Specificity** & **AUC** \\ \hline [PERSON] et al. [73] 2017 & 81.30 & 78.70 & 79.70 & - & - \\ \hline [PERSON] et al. [30] 2021 & 93.00 & - & - & - & - \\ \hline [PERSON] et al. [74] 2022 & - & 92.00 & 92.00 & - & 96.00 \\ \hline Proposed work & 98.15 & 97.33 & 93.59 & 98.35 & 97.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of proposed work for the classification of melanoma vs. nevus with existing work on ISBI 2016 dataset. All values are in percentages. The symbol “-” indicates missing information. As can be seen, the network focuses mainly on the pixels of the skin lesion; from here emerges the contribution of adding texture features extracted not only from the lesion but also from what surrounds it, which is not automatically considered by the neural network and which could be useful for the correct classification of the lesion. However, injecting into the network additional features extracted from images that have not undergone any preprocessing of cropping out parts that are not relevant to the diagnosis may lead the network to attribute to one of the two classes a specific feature that is not related to the pathology at all. These are spurious correlations, and in the present case, these correlations involve dark image contours due to the image acquisition process and associated with the class of melanoma (Figure 8). Such images are classified correctly, but interpretability analysis shows that to arrive at the correct result, even before feature injection, the network focuses on regions of the image that are not diagnostically important. If the network already sees these details as possible important features for assigning the N or M label to the images, it is possible that the additional feature vector is going to contribute in the same direction by emphasizing the spurious correlation. One would then need to conduct an analysis on the outcome of the classification after appropriately cropping the images so as to eliminate the black edge disturbance while retaining as much of the surrounding tissue as can be saved. Another important aspect to consider is that the proposed model is among the methodologies that can classify images of single skin lesions but not total body images.