Early and accurate detection of melanoma skin cancer using hybrid level set approach - Ragab, Mahmoud; Choudhry, Hani; Al-Rabia, Mohammed W.; Binyamin, Sami Saeed; Aldarmahi, Ahmed A.; Mansour, Romany F. (Frontiers in Physiology, 2022)
Digital dermoscopy is used to identify cancer in skin lesions, sun exposure is one of the leading causes of melanoma. It is crucial to distinguish between healthy skin and malignant lesions when using computerised lesion detection and classification. Lesion segmentation influences categorization accuracy and precision. This study introduces a novel way of classifying lesions. Hair filters, gel, bubbles, and specular reflection are all options. Improved level set method is employed in an innovative method for detecting and removing cancer hairs. The lesion is distinguished from the surrounding skin by the adaptive sigmoidal function, this function considers the severity of localised lesions. An improved technique for identifying a lesion from surrounding tissue is proposed in the article followed by a classifier and available features resulted in 94.40% accuracy and 93% success. According to research, the best method for selecting features and classification, which can produce more accurate predictions before and during treatment. The recommended strategy is put to the test using Melanoma Skin Cancer Dataset, the recommended technique outperforms the alternative.