## 4 Results and Discussion  
### Model Performance Evaluation  
The model achieved the lowest validation loss of 0.0685 at epoch 11 and the highest validation accuracy of 98.30% at epoch 23. The model accuracy increased significantly from the beginning and stabilized after epoch 11, with the learning rate gradually decreasing at each epoch to avoid overfitting. The model can generalize quite well, and the stability and consistency gap between training and validation show good results, as shown in Figure 7.  
The model performed very well, with most predictions falling within the main diagonal of the confusion matrix, indicating a high level of accuracy. However, there were some significant misclassifications in class 4, with 103 samples misclassified as class 6, 50 samples misclassified as class 2, and 17 samples misclassified as class 1. In the other classes, these were very small or zero, indicating that the model had excellent precision and recall for most of the class categories shown in Figure 8. Improvements can be made by further \begin{table}
\begin{tabular}{c c c} \hline
**Hardware Specification** & **Raspberry Pi 5 Model B Rev 1.0** & **NVIDIA Jetson Nano 4.6.6** \\ \hline CPU & Broadcom BCM2712 2.4 GHz & Quad-core ARM Cortex-A57 \\  & Quad-core Cortex-A76 & MP-Core \\ GPU & VideoCore VII GPU & NVIDIA Maxwell architecture \\  & & and 128 CUDA cores \\ RAM & 8 GB LPDDR4X-4267 SDRAM & 4 GB 64-bit LPDDR4, \\  & 1600 MHz 25.6 GB/s \\  & Micro SDHC 64 GB & microSDHC 64 GB \\ OS & Debian 12.2 GNU/Linux & JetPack 4.6.6 (Ubuntu 18.04) \\  & rasp-berryi 6.6.6 & 5 V/2 A DC power via \\ Adaptor & 5 V/5 A DC power via USB-C & Micro-USB \\ \hline \end{tabular}
\end{table} 
Table 1: Hardware specifications. Figure 7: Metrics of (**a**) training and validation accuracy and (**b**) training and validation loss. exploring the features that distinguish classes 4, 6, and 2, and by increasing the amount of other training data to reduce the bias between classes. Compared to other models in Table 2, the model proposed in this paper shows the best performance with 98% accuracy, surpassing the CNN method in [PERSON] and [PERSON], (2023) [10] with dataset PH2 got 93.33% accuracy and in research [PERSON] et al. (2024) [13] used dataset ISIC got 89.31% accuracy. With same CNN method used HAM10000 dataset in [PERSON] et al. (2023) [3] got 97.29% accuracy. This model is also superior to the TinyML method in the paper by [PERSON] et al. (2024) [16] with the lowest accuracy of 85% and CNN with Alexnet method achived high accuracy at 98% in [PERSON] et al. (2023) [14]. In addition, the proposed model is successfully implemented on a single-board computer (SBC), as well as several other models, which show efficiency in deployment. Our proposed model achieves the highest accuracy, but also maintains implementation flexibility, making it a superior solution in the classification of the HAM10000 dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Method** & **Dataset** & **Accuracy (\%)** & **SBCs Platform** \\ \hline [PERSON], [PERSON], and & & & & \\ [PERSON] [3] & CNN, VGG-19 & HAM10000 & 97.29\% & No \\ [PERSON], [PERSON], and [PERSON] [14] & CNN, Alexnet & ISIC & 98.0\% & No \\ [PERSON] et al. [16] & TinyML & ISIC 2020 & 85\% & Yes \\ [PERSON] et al. [13] & CNN & ISIC & 89.31\% & Yes \\ [PERSON] and [PERSON] [10] & CNN & PH2 & 93.33\% & Yes \\ \hline Proposed Model & CNN & HAM10000 & 98.25\% & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of proposed model with state of the art. Figure 8: Confusion matrix of proposed model. The results of our experiment illustrate both the value and effectiveness of the suggested convolutional neural network (CNN) model for skin cancer classification. When tested on the HAM10000 dataset, as shown in Table 3, the model attained an impressive overall accuracy of 98%, surpassing other cutting-edge approaches significantly. In terms of individual class performance, the model achieved high precision, recall, and F1-scores in all seven disease categories.