Deep Learning-Based Approach for Skin Lesion Classification and Melanoma Detection

Abstract

Skin cancer is one of the most frequent malignancies in the world, and its incidence is rising as people age. In general, it is best to diagnose skin cancer as soon as possible. Melanoma is a dangerous form of skin cancer that has been more prevalent worldwide over the past few decades. As a result of medical professionals' efforts to find a cure, automatically identifying skin lesions using dermoscopic pictures has remained a difficult and complex task. Indistinct lesion borders, poor color contrast, location dependence, form fluctuations, and complicated lesion structures are some of the variables that contribute to this type of difficulties in lesion diagnosis. Medical personnel and researchers can save many lives if the growing public health burden issues are identified early and treated appropriately to stop them from spreading to other body organs. There is a possibility that the person may have melanoma if there is an unusual change in the skin's appearance. Dermatology expertise must be integrated with computer vision methods for effective melanoma detection in order to achieve improved results. Therefore, it's critical to create a variety of detection methods to help medical professionals identify melanoma in its early stages. The proposed model presents an intelligent and integrated framework for automated melanoma detection that combines adaptive pre-processing, information-theoretic segmentation, discriminative feature extraction, and optimized deep classification. An Information-Gain–Driven Morphological–ABCD Feature Fusion (IGM-ABCD-FF) framework is proposed to adaptively refine clinically significant lesion features prior to deep residual classification.