# Exploring Pre-Trained Models for Skin Cancer Classification
## 4 Results and Discussions
Integrated gradients tend to produce more granular and detailed heatmaps, whereas LIME focuses on broader, contiguous regions. This distinction makes integrated gradients more suitable for analyzing fine-grained patterns, while LIME offers more intuitive visual explanations by outlining specific lesion areas. Together, these techniques provide complementary insights, enhancing the overall interpretability of the model's predictions. Table 2 presents a comparative analysis of previous studies that utilized the same dataset for skin lesion classification, highlighting the models and their corresponding accuracy rates. The results demonstrate a noticeable variation in performance across different architectures and timeframes. Early studies, such as [PERSON] et al. [37] (2019), achieved relatively high performance with InceptionV3 (87.42%), surpassing ResNet-101 (84.09%). [PERSON] et al. [40] (2019) also employed InceptionV3, reporting an accuracy of 86%, which aligns closely with [PERSON] et al. 's findings [37]. However, [PERSON] [39] (2021) obtained a significantly lower accuracy of 54% using a CNN model, possibly indicating limitations in model architecture or data preprocessing techniques. \begin{table}
\begin{tabular}{c c} \hline \hline
**Study and (Year)** & **Model and (Accuracy)** \\ \hline [PERSON] et al. [37] (2019) & ResNet-101 (84.09\%) and Inceptionv3 (87.42\%) \\ \hline \multirow{2}{*}{[PERSON] et al. [38] (2023)} & VGG16 (70.17\%), ResNet50 (86.57\%), and InceptionV3 (91.26\%) \\ \hline [PERSON] [39] (2021) & CNN (54\%) \\ \hline [PERSON] et al. [40] (2019) & Inception-v3 (86\%) \\ \hline [PERSON] [41] (2023) & CNN (80\%) and Xception (80\%) \\ \hline \multirow{2}{*}{[PERSON] et al. [42] (2023)} & AlexNet (88.33\%), ResNet18 (89.39\%), SqueezeNet (85.91\%), and ShuffleNet (87.42\%) \\ \hline \multirow{2}{*}{[PERSON] et al. [28] (2024)} & Vgg19 (85.6\%), ResNet18 (82.4\%), ResNet50 (8.8\%), \\  & DensNet201 (83.2\%), MobileNetv2 (85.3\%), ViT\_b (88.6\%), ViT\_L (88.6\%), Swin\_s (87.7\%), Swin\_b (87.8\%), and Swin\_T (87.7\%) \\ \hline \multirow{2}{*}{This study} & ResNet50 (89.09\%), VGG19 (86.21\%), ViT\_b16 (86.97\%), and ViT\_b32 (83.33\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of related previous studies utilized the same dataset. More recent studies exhibit improved performance, suggesting advancements in model architectures and training strategies. [PERSON] et al. [38] (2023) achieved 91.26% with InceptionV3, outperforming older models and demonstrating the ongoing relevance of this architecture. [PERSON] et al. [42] (2023) explored multiple architectures, with ResNet18 achieving the highest accuracy of 89.39%, followed by AlexNet (88.33%), SqueezeNet (85.91%), and ShuffleNet (87.42%), indicating the effectiveness of lightweight models. [PERSON] et al. [28] (2024) presented a comprehensive evaluation across various models, with ResNet50 (88.8%) and vision transformers (ViT_b and ViT_L) both achieving 88.6%, showcasing the potential of transformer-based architectures in medical imaging tasks. This study is consistent with [28] in comparing the CNN and ViT techniques. However, we have noted improvements in the performance of certain models in our work. These enhancements are due to differences in the initial data processing and the approach to training the models. This suggests that performance can be further enhanced by incorporating different methods and modifications. Moreover, we have applied alternative interpretation techniques, providing a wider view and a more thorough comparison for understanding the same models. In this study, the best performance was achieved with ResNet50 (89.09%), exceeding the accuracies reported by previous works, including [PERSON] et al. [38] (2023) and [PERSON] et al. [42] (2023). The ViT_b16 model achieved 86.97%, demonstrating competitive performance compared to traditional CNNs. Although pre-trained CNNs and ViTs are highly effective at classifying skin cancer, some limitations remain.