# Exploring Pre-Trained Models for Skin Cancer Classification
## 2 Related Works
Their study found that Grad-CAM achieved the lowest computational cost, while LIME required the highest execution time. These works provided diverse perspectives on the use of XAI for skin cancer classification, emphasizing the balance between accuracy, stability, interpretability, and clinical applicability. To make XAI techniques truly reliable in clinical practice, there is still a need for further research on ensuring the reproducibility and stability of explanations across different datasets and imaging conditions. Recently, transformers have emerged in medical image analysis, being increasingly utilized for disease diagnosis and clinical applications [26]. Several studies have investigated transformer-based architectures in comparison to CNNs. [PERSON] and [PERSON] [27] evaluated vision transformers (ViTs) against convolutional models for skin cancer classification, suggesting that ViTs may outperform CNNs, particularly on large datasets. [PERSON] et al. [28] explored skin lesion classification using various pre-trained models, including ViTs, Swin Transformers, and CNNs. [PERSON] [29] compared ResNet18 and ResNet50 with ViTs for skin lesion classification, demonstrating that ViTs achieved superior performance metrics; however, a major limitation was their longer training time. [PERSON] [30] investigated deep learning methods for automatic skin lesion classification, comparing CNN architectures (VGGNet and ResNet) with transformer-based models (ViT and DeepViT). Their findings indicated that CNNs still outperformed ViTs in skin cancer classification, despite ViTs' promising capabilities. The reviewed studies underscore the critical role of pre-trained deep learning models in skin cancer classification and their potential for early detection. The continuous advancement of these models holds significant promise for improving diagnostic accuracy, ultimately enhancing patient survival rates and expediting recovery. Furthermore, most studies highlight the importance of eXplainable AI (XAI) techniques in ensuring transparent and interpretable model predictions, thereby increasing confidence in automated diagnostic systems. However, further research and continuous innovation are essential to fully harness the potential of deep learning and XAI in skin cancer diagnosis. Previous works have primarily concentrated on CNNs, with only a few studies exploring ViTs and comparing them to CNNs. These studies have highlighted discrepancies in the results of CNN and ViT comparisons, influenced by factors such as data size and type, preprocessing methods, and variations in model training parameters. In addition, there is an ongoing need to identify strategies to improve these models for better performance, particularly in the detection and diagnosis of skin cancers. This area remains underexplored and warrants further investigation. The proposed method aims to address this gap by evaluating both CNN-based models (ResNet50 and VGG19) and transformer-based architectures (ViT-b16 and ViT-b32) for skin lesion classification. This approach contributes to the enhancement of pre-trained models, the advancement of ViT techniques, and their application in skin cancer diagnosis, ultimately leading to a more comprehensive understanding of their clinical relevance.