Skin Cancer Detection and Classification Using Deep Learning

Abstract

Abstract: Skin cancer is the most prevalent type of cancer worldwide. Early skin cancer identification is essential for effective therapy ,and computer-aided diagnosis systems can improve the precision and effectiveness of diagnosis. In this study, a Convolutional Neural Network (CNN) is developed for skin cancer detection. The CNN is trained on a dataset of skin lesion images to classify the lesions as either cancerous or noncancerous. The proposed model achieved an accuracy of 90% in classifying skin rashes as benign or malignant, demonstrating its potential as a tool for skin cancer detection. Skin cancer is the out-of-control development of unusual cells in the epidermis, the outermost skin layer, brought about by DNA harm that causes harmful variations. Despite consistent upgrades in medication, skin cancer is still an issue. Consequently, the insights by the Skin Cancer Foundation, one of every five Skin cancer will occur in people by age seventy. The project expects to plan a framework that will be adequately proficient to distinguish the occurrences of different sorts of skin malignancy in the body by extracting significant patterns from the dataset.