ENHANCING DERMATOLOGICAL IMAGING: A DEEP LEARNING APPROACH FOR SKIN CANCER DETECTION AND MULTI-CLASS CLASSIFICATION

Abstract

Since skin cancer is a common and sometimes fatal condition, prompt diagnosis is essential to effective treatment. We offer a deep learning model in this work for the multi-class categorization and detection of skin cancer lesions. A major global health concern, skin cancer is diagnosed using traditional methods that mostly rely on dermatologists' knowledge, which can be difficult to use, subjective, and cause delays in diagnosis. These disadvantages may cause opportunities for early intervention to be lost. We suggest a deep 
learning-based system to automatically identify and classify skin cancer lesions into melanoma, basal cell carcinoma, and squamous cell carcinoma, among other categories, in order to solve these problems. In addition to overcoming the drawbacks of traditional approaches, our suggested solution provides scalability, consistency, and the possibility of wider accessibility, all of which could enhance the precision and promptness of skin cancer diagnosis. We assess the model using an extensive dataset, proving its usefulness in supporting dermatologists and other medical professionals in the early detection and management of skin cancer, ultimately leading to better patient outcomes and more efficient healthcare delivery.