G. Vijayakumari, CYRIL MATHEW O
Mortality of skin cancer which is one of the most prevalent cancers in the world is greatly minimized by its early diagnosis. Recent developments in artificial intelligence (AI), specifically deep learning, have displayed a lot of potential in the process of automating the process of identifying and classifying skin lesions on the basis of medical images. In this proposal, a Skin Lesion Identification using Deep learning (SLI-DL) model, which operates on the basis of electrochemical Techniques, is proposed to assist dermatologists in making quality and timely diagnostic decisions. Electrochemical techniques are analytical methods used to study chemical properties through electrical signals. The framework applies CNNs for skin lesion analysis through image preprocessing and classification into benign, malignant, or suspicious types. Trained on labeled dermoscopy datasets, pre-trained models such as VGGNet and ResNet improve accuracy. Moreover, it has real-time feedback, which allows analysing lesions instantly and making a quick clinical decision. This is an overall method with lesion segmentation, localization and classification extending the diagnostic range of a variety of dermatological disorders.