Towards Accurate Skin Cancer Screening: A CNN Model for Dermatological Images

Early detection is vital for effective treatment and increased survival rates in the case of skin cancer, which is a common type of cancer globally. Machine learning (ML) has become a promising tool in the field of dermatology, providing improved precision and effectiveness in the screening of skin cancer. This assessment examines the different classification methods used in machine learning for detecting skin cancer, discussing the techniques, outcomes, and obstacles linked to these approaches. The research investigates widely utilized algorithms like CNNs, SVMs, and ensemble methods, analyzing how they are utilized in distinguishing between cancerous and non-cancerous growths. It also touches upon the significance of image preprocessing, feature extraction, and data augmentation in enhancing classification performance. In addition, the analysis discusses present constraints, such as a lack of data, prejudice, and the necessity for transparency in ML models, emphasizing topics for further study. This paper aims to lead future advancements in ML-based skin cancer screening by offering a comprehensive summary of the current state of the field, resulting in more precise, available, and scalable diagnostic options.