Comprehensive Study on Skin Cancer Detection and Classification Using Machine Learning

The human body's largest organ, the skin, plays a critical role in protecting internal tissues from external factors. Skin cancer is a prevalent condition, and while these cancers are generally treatable, their progression can become fatal if patients do not receive appropriate treatment. Current medical detection methods rely on human examinations, which can be slow, error-prone, and lead to examiner fatigue. This study explores the potential of integrating Convolutional Neural Networks (CNNs) with image processing techniques to facilitate rapid and accurate detection and classification of skin cancer. The review discusses various machine learning methods and models that support real-time detection of different skin cancers. Automated diagnostic systems offer early disease warnings, reduce the need for chemical treatments, and enhance skin health, ultimately improving medical diagnostic capabilities. The paper also addresses the challenges associated with existing models and emphasizes the need for training on diverse datasets, proposing enhancements for broader implementation in future applications.