Skin Lesions Classification Using CNN

In dermatology, skin lesions present a diagnostic challenge that requires precise classification for efficient pa- tient care. Conventional categorization techniques that depend on dermatologists’ subjective visual assessment are prone to inaccuracies and inconsistencies. This work proposes a novel way to automate the classification of skin lesions using image processing algorithms, with a focus on meeting the needs of the older population, who frequently face obstacles while trying to access healthcare services. The suggested system uses state-of-the- art classification algorithms, such as machine learning and deep learning models, to accomplish accurate categorization. It also includes preprocessing approaches to improve image quality and feature extraction methods to capture pertinent lesion features. The goal of this research project is to greatly increase diagnosis accuracy and efficiency by automating the classification process. This would ultimately assist patients and medical personnel, especially in scenarios where direct contact is limited, like pandemics. Future approaches should focus on making sure that data security measures are strong and improving user interface design for senior users.