I. Introduction
Skin cancer, particularly melanoma, presents a growing global health challenge, emphasizing the critical need for early detection to improve patient outcomes. Melanoma is a serious form of skin cancer that originates in the melanocytes, the cells responsible for producing melanin, the pigment that gives skin its color. It is known for its ability to spread rapidly to other parts of the body if not detected and treated early. Melanoma often manifests as a change in an existing mole or as a new, unusual-looking growth on the skin. Risk factors include excessive exposure to ultraviolet (UV) radiation from the sun or tanning beds, having a fair complexion, a history of sunburns, and a family history of the disease [1]. Early detection through regular skin checks and prompt medical attention is crucial for effective treatment and improving survival rates. Recent advancements in artificial intelligence (AI) and deep learning have paved the way for innovative solutions in healthcare, such as AI-powered models for early skin cancer diagnosis [2]. This study aims to integrate deep learning algorithms into dermatology to establish an efficient system for detecting melanoma in its early stages. Melanoma, originating from pigment-producing melanocytes is renowned for its aggressive nature; however, early identification significantly enhances treatment prospects and patient outcomes [3]. The proposed AI-powered model for skin cancer diagnosis leverages learning techniques to analyze high-resolution dermatoscopic images, capturing lesion details crucial for diagnosis. Trained on a dataset of such images, the deep learning model discerns intricate patterns indicative of melanoma, providing a powerful diagnostic tool. AI integration in melanoma detection offers the potential to expedite diagnostics, leading to earlier interventions and improved outcomes, while bolstering diagnostic accuracy and supporting healthcare decision-making [4]. This automated approach to early melanoma detection using advanced AI techniques addresses the time-consuming and error-prone nature of manual examination methods [5]. Through image preprocessing steps and deep learning-based Convolutional Neural Networks, the system accurately extracts features from skin lesion images, facilitating early melanoma detection with enhanced efficiency and accuracy [6]. The system's advantages over traditional methods include error reduction, faster diagnosis, and robust detection mechanisms, ultimately contributing to improved patient care [7].