EXPLORING DEEP LEARNING TECHNIQUES FOR ADVANCED SKIN CANCER DETECTION AND MULTI-CLASS CLASSIFICATION IN DERMATOLOGICAL IMAGING

Abstract

Given the prevalence and occasionally lethal nature of skin cancer, early detection is crucial for successful treatment. In this study, we propose a deep learning model for the multi-class classification and identification of skin cancer lesions. Skin cancer, a serious worldwide health issue, is detected using conventional techniques that mostly rely on dermatologists' expertise. These techniques can be challenging to use, subjective, and lead to delays in diagnosis. These drawbacks might result in missed chances for early intervention. To address these issues, we propose a deep learning-based system to automatically detect and categorize skin cancer lesions into many categories, including melanoma, basal cell carcinoma, and squamous cell carcinoma. Apart from surmounting the limitations of conventional methods, our proposed system offers scalability, consistency, and potential for increased accessibility, all of which have the potential to improve the accuracy and timelyness of skin cancer detection. We evaluate the model on a large dataset, demonstrating its utility in aiding dermatologists and other health care providers in the early diagnosis and treatment of skin cancer, which would eventually improve patient outcomes and streamline healthcare delivery.