# Exploring Pre-Trained Models for Skin Cancer Classification
## 1 Introduction  
Skin cancer is a prevalent condition that can manifest with symptoms such as burning, bleeding, and itching. While survival rates remain high, the increasing number of diagnoses has become a global concern [1]. It is estimated that approximately one in five individuals will develop skin cancer during their lifetime. Early detection and effective management have significantly reduced mortality rates. Skin cancers are primarily categorized into two types: non-melanoma and melanoma [2]. Early diagnosis substantially increases the likelihood of successful treatment for all skin cancer types. However, distinguishing between benign and malignant skin lesions remains a challenge, even for experienced dermatologists [3]. Recent advancements in computer vision and deep learning have demonstrated promising results in localizing and classifying skin lesions, enabling faster and more accurate analysis of dermatological images. These technologies aim to enhance skin cancer detection and facilitate timely, personalized treatment [4]. The conventional process for diagnosing skin cancer involves image acquisition, preprocessing, segmentation, feature extraction, and classification. Artificial intelligence (AI) has made early skin cancer detection increasingly feasible, reducing cancer-related morbidity and mortality [5]. Over the past decades, various Computer-Aided Diagnosis (CAD) systems have been developed for skin cancer detection. Traditional computer vision techniques primarily relied on feature extraction based on texture, color, and shape. More recently, deep learning, particularly convolutional neural networks (CNNs), has emerged as a powerful tool, achieving remarkable performance in skin cancer classification [6]. Moreover, research and development efforts have increasingly focused on integrating attention mechanisms into CNN-based architectures. These attention-driven transformer models have gained traction due to their ability to capture long-range dependencies and learn complex feature representations [7]. Transformers, originally developed for sequence modeling and machine translation, have become the leading deep learning models for numerous Natural Language Processing (NLP) tasks. The latest advancements in this domain include self-supervised transformers, which are pre-trained on large datasets and later fine-tuned on smaller datasets for specific applications [8]. Following their success in NLP, transformers have been increasingly adopted in computer vision. Vision transformer (ViT) models were among the first to achieve performance comparable to CNNs, processing images as sequences of patches. Inspired by ViT's success, several modified architectures and training strategies have been proposed, leading to significant improvements in transformer-based vision models [9]. With the growing interest in machine learning, deep learning, and neural networks, automated skin cancer classification has become a crucial area of research [10]. However, a major challenge in AI-based medical applications is the lack of transparency in algorithmic decision making. To address this issue, eXplainable Artificial Intelligence (XAI) techniques have been introduced to enhance model interpretability. XAI provides insights into the reasoning behind model predictions, improving trust in AI-driven diagnostic systems and fostering collaboration between AI experts and medical professionals [11]. In precision medicine, explainability is essential, as clinicians require more than just binary classifications--they need detailed insights to support their diagnostic decisions [12]. Accurately distinguishing malignant from benign skin lesions remains a significant challenge in dermatology. Deep learning and eXplainable Artificial Intelligence (XAI) have shown considerable potential in improving diagnostic accuracy and transparency.