Skin lesion segmentation: A systematic review of computational techniques, tools, and future directions

Skin lesion segmentation is a highly sought-after research topic in medical image processing, which may help in the early diagnosis of skin diseases. Early detection of skin diseases like Melanoma can decrease the mortality rate by 95%. Distinguishing lesions from healthy skin through skin image segmentation is a critical step. Various factors such as color, size, shape of the skin lesion, presence of hair, and other noise pose challenges in segmenting a lesion from healthy skin. Hence, the effectiveness of the segmentation technique utilized is vital for precise disease diagnosis and treatment planning. This review explores and summarizes the latest advancements in skin lesion segmentation techniques and their state-of-the-art methods from 2018 to 2025. It also covers crucial information, including input datasets, pre-processing, augmentation, method configuration, loss functions, hyperparameter settings, and performance metrics. The review addresses the primary challenges encountered in skin lesion segmentation from images and comprehensively compares state-of-the-art techniques for skin lesion segmentation. Researchers in this field will find this review compelling due to the insights on skin lesion segmentation and methodological details, as well as the encouraging results analysis of the state-of-the-art methods.