Melanoma Skin Cancer Identification Using Machine Learning Technique

Melanoma, a type of skin cancer originating from the cells that produce melanin, has witnessed an alarming rise in its incidence globally, necessitating efficient and accurate detection strategies Being detected early plays a pivotal role in patient prognosis, making the development of precise diagnostic tools imperative. In this research endeavor, we explore the application of optimization algorithms to improve the accuracy and efficiency of melanoma detection using dermoscopy imaging. Leveraging advanced computational techniques, we seek to optimize preprocessing steps, feature extraction, and classification processes. Integration of these optimization methodologies aim to refine the quality of dermoscopy images, extract salient features characteristic of melanoma lesions, and facilitate robust classification using machine learning and deep learning models. This research study intends to contribute to an automated and efficient melanoma detection system, offering early diagnosis and potentially saving lives through timely intervention.