# Exploring Pre-Trained Models for Skin Cancer Classification
## 2 Related Works  
Deep learning has significantly advanced machine learning, with deep neural networks playing a crucial role in skin cancer detection through various computer-based approaches [13]. [PERSON] et al. [14] applied convolutional neural networks (CNNs), utilizing transfer learning models such as ResNet50, InceptionV3, and Inception-ResNet, to distinguish between malignant and benign skin lesions. Their approach demonstrated high classification performance in identifying skin lesions. Similarly, [PERSON] et al. [15] evaluated multiple CNN architectures and employed data augmentation, transfer learning, and fine-tuning to address challenges such as class imbalance and image similarity. Their best-performing model was integrated into a mobile application for real-time skin lesion classification, also offering personalized sun exposure recommendations. Additionally, [PERSON] and [PERSON] [16] developed an automated Computer-Aided Diagnosis (CAD) system for skin lesion classification, achieving high accuracy while maintaining low computational complexity. Several pre-trained CNNs were assessed, with ResNet50 combined with a Support Vector Machine (SVM) yielding the best results. Deep learning has also been widely applied in oncology to improve diagnosis and treatment planning. However, human involvement remains essential for clinical adoption. To be effectively integrated into medical workflows, deep learning models must not only achieve high predictive accuracy but also ensure interpretability and provide an estimate of prediction uncertainty [17]. A common challenge is that deep learning models are often perceived as black-box systems, raising concerns regarding potential biases, particularly in medical applications. This has led to a growing demand for eXplainable Artificial Intelligence (XAI) techniques to enhance the interpretability of these models [18]. Several explanation methods have been developed to generate heatmaps, highlighting important image regions by backpropagating gradients and analyzing how changes in individual pixels influence the model's decision-making process [19]. Regarding XAI techniques in skin cancer detection, [PERSON] et al. [20] conducted a systematic review on the integration of XAI in deep neural networks (DNNs) for skin lesion classification, focusing on common visualization techniques. However, there remains a lack of comprehensive evaluations on the effectiveness of XAI in deep learning-based cancer detection, particularly concerning its impact on human decision making and confidence in CAD systems. Several researchers have explored various XAI methodologies to enhance model interpretability. [PERSON] et al. [21] proposed an XAI-based skin lesion classification system aimed at improving the early detection of skin cancer. By incorporating LIME, the model provided interpretable visual explanations, enhancing its applicability in clinical environments. [PERSON] and [PERSON] [22] compared two local explanation techniques--integrated gradients and LIME--for skin lesion classification. Their findings indicated that while both methods demonstrated strong local fidelity, integrated gradients exhibited greater stability and robustness, whereas LIME provided more intuitive explanations. [PERSON] et al. [23] analyzed the interpretability of LIME and SHAP (Shapley Additive Explanations) in melanoma classification, assessing their reproducibility and execution time. Their resultsindicated that LIME outperformed SHAP in both aspects. [PERSON] et al. [24] examined six saliency methods (integrated gradients, Kernel SHAP, Occlusion, RISE, Grad-CAM, and Attention) on a melanoma dataset, introducing two novel metrics: perceptual interpretability (visual coherence) and semantic interpretability (overlap with expert-identified features). Similarly, [PERSON] et al. [25] evaluated three XAI techniques--integrated gradients, LIME, and Grad-CAM--on CNN models for lung cancer classification.