Segmentation and Classification of Skin Cancer Diseases Based on Deep Learning: Challenges and Future Directions

Deep Learning (DL) techniques have significantly improved the diagnostic accuracy in healthcare, particularly for detecting and classifying skin cancer. These advancements are expected to assist healthcare professionals in delivering more accurate, efficient, and timely diagnosis, ultimately improving patient outcomes and facilitating early detection and treatment of skin cancer. Additionally, medical imaging technologies, such as magnetic resonance imaging (MRI) and computed tomography (CT) scans remain critical tools for diagnosing dermatological conditions. However, interpreting these images can be challenging because of the overlapping structures and varying image quality. This study explored the application of DL in skin cancer diagnosis, with a particular focus on advances in image segmentation and classification. It reviews the development of DL-based models, including Convolutional Neural Networks (CNNs), and evaluates their effectiveness in skin lesion detection. This study examined the critical challenges associated with deploying DL models in clinical practice, including dataset diversity, model interpretability, and the feasibility of real-world implementation. It explores essential factors such as the selection of network architectures and data preprocessing techniques, emphasizing their influence on model performance. Additionally, this study identifies research gaps and suggests future directions for optimizing DL models for dermatological applications.