# Integrating Explanations into CNNs by Adopting Spiking Attention Block for Skin Cancer Detection
## 4 Experimental Results
### Ablation Analysis  
When the model was evaluated using various train-test splits on the HAM10000 dataset using the InRe + SAB model, the model trained using 85% of the data outperformed the models developed using 80% and 70% of the data, as indicated in Table 2. Therefore, we picked the \(85-15\) dataset for the purpose of the training-validation split to conduct the experiments. The scatter plots in Figure 16 reveal a general trend of higher precision associated with higher AUC values, indicating robust model performance in certain scenarios. For instance, the 'Akiec' class consistently shows high precision and AUC, particularly in the Split = 15 scenario, reflecting the model's strong capability to accurately classify actinic keratosis and intra-epithelial carcinoma with minimal false positives. However, there are classes and splits where the precision significantly drops while maintaining a relatively high AUC, such as the 'nv' (melanocytic nevi) in Split = 30, suggesting a possible overfitting or imbalance in the model's ability to generalize this class under varied data conditions. The variability in performance across different splits and classes underlines the importance of tailored model training and validation strategies, especially for medical diagnostic applications where precision is critical. The visual markers enhance interpretability by \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Split = 15**} & \multicolumn{2}{c}{**Split = 20**} & \multicolumn{2}{c}{**Split = 30**} \\ \cline{2-7}  & **AUC** & **Precision** & **AUC** & **Precision** & **AUC** & **Precision** \\ \hline
**akiec** & 1.0 & 1.00 & 0.97 & 0.95 & 0.98 & 0.89 \\ \hline
**bcc** & 0.99 & 0.95 & 0.92 & 0.83 & 0.99 & 0.78 \\ \hline
**bkl** & 0.94 & 0.92 & 0.96 & 0.68 & 0.96 & 0.62 \\ \hline
**df** & 0.96 & 1.00 & 0.98 & 0.94 & 0.93 & 0.89 \\ \hline
**nv** & 0.92 & 0.89 & 0.91 & 0.59 & 0.94 & 0.53 \\ \hline
**mel** & 1.0 & 0.97 & 0.99 & 0.88 & 0.97 & 0.81 \\ \hline
**vasc** & 1.0 & 1.00 & 1.0 & 0.93 & 0.98 & 0.87 \\ \hline
**Macro Average** & 0.97 & 0.89 & 0.96 & 0.82 & 0.96 & 0.77 \\ \hline
**Weighted Average** & 0.98 & 0.96 & 0.96 & 0.92 & 0.93 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different split ratios using the InRe + SAB model on the HAM10000 dataset. Figure 15: Attention maps generated using InRe+SAB on the HAM10000 dataset. distinctively showcasing the performance metrics of each class, enabling rapid assessment and comparison across different experimental conditions. In addition, the proposed approach is compared to the state-of-the-art models for skin cancer classification on the HAM10000 dataset, as shown in Table 3. In [29], the authors introduced an innovative Multi-weighted New Loss technique to address class imbalance and enhance the accuracy of detecting critical classes, such as melanoma. They suggested an innovative end-to-end cumulative learning technique that more successfully balances representation learning and classifier learning without incurring extra processing costs. The suggested technique achieved 92% accuracy and 94% AUC on the HAM10000 dataset. In [30], the authors introduced a high-performance data augmentation technique for a search space, which can be integrated with any model via a plug-and-play approach to identify the optimal augmentation method for a medical database with minimal resource expenditure. The integration with EfficientNets resulted in an accuracy of 86% and an AUC of 97% on the HAM10000 dataset. The authors of [31] introduced an automated model for skin lesion categorization with dermoscopic pictures, employing convolutional neural networks (CNNs) and including augmentation methods such as cut-out, random shear, random scaling, random rotation, random flipping, contrast adjustments, and random brightness modifications. The suggested model attained 85% accuracy and 97% AUC.