Skin Cancer: Epidemiology, Screening and Clinical Features of Acral Lentiginous Melanoma (ALM), Melanoma <i>In Situ</i> (MIS), Nodular Melanoma (NM) and Superficial Spreading Melanoma (SSM)
Chun‐Te Lu, Teng‐Li Lin, Arvind Mukundan, Riya Karmakar, A. Chandrasekar, Wen‐Yen Chang, Hsiang‐Chen Wang
Abstract
(MIS), nodular melanoma (NM), and superficial spreading melanoma (SSM). This study assesses the epidemiology, clinical characteristics, and screening techniques related to various melanoma subtypes, emphasizing their distinct features and risk factors. Moreover, the use of machine learning (ML) methodologies to categorize melanoma subtypes and the thorough examination of advancements in AI-based melanoma diagnosis, primarily emphasizing convolutional neural networks (CNN) and transfer learning approaches. Evaluate the efficacy of several deep learning models in classifying melanoma subtypes while addressing significant obstacles, including class imbalance and model generalization. Furthermore, it contemplates the integration of multimodal data, including genetic information and patient demographics, to enhance diagnostic accuracy. This comprehensive review assesses the epidemiology, clinical characteristics, and machine learning techniques utilized for the classification and detection of different melanoma subtypes, emphasizing recent advancements in AI-driven methods and their clinical significance.