A CNN Model For Skin Cancer Detection And Classification By Using Image Processing Techniques

Abstract

Skin cancer is type of cancer that grows in the skin tissue, which can damage to the surrounding tissue,
can cause disability and even death. Skin cancer is essential health jeopardy that requires early detection
intended effectual treatment. It impacts a vast population globally, necessitating timely and accurate
diagnosis for effective treatment. Proposed work introduces an pioneering approach to computerized skin
cancer detection through the integration of sophisticated machine learning technique into a Flask web
application. The CNN premeditated to analyze skin images & classify them into specific cancer categories
proficiently. The Flask web application, a user-friendly interface that allows individuals to easily upload
images of their skin conditions and also offers the capability toward exploit webcam intended live image
uploads. Through combine sophisticated machine learning technique with user-centric web application,
represents a significant step towards making skin cancer identification more accessible and accurate,
potentially improving healthcare outcomes. Model achieves an accuracy of 90.73%. As result, study
shows a significant outcome of using CNN replica in detect skin cancer.