Research Report on Melanoma Skin Cancer Detection Using CNN Algorithm and Image Processing

Skin cancer is a serious problem that is frequently ignored. When a clinician performs a manual examination, Using imaging data, the human eye might not always be able to correctly identify ailments. In today's environment, Multi layered networks are being employed more and more to solve difficulties in our everyday lives. Therefore, we develop an automated computerised system for detecting skin problems using deep neural network approaches. We have incorporated several neural network algorithms into the suggested model and evaluated their results to ascertain the most precise method for recognising the five main skin conditions.CNN, and by using the Keras Sequential API, we have created a new model that achieves an accuracy of roughly 85%. We have since used approaches that make use of pre-trained data to increase accuracy and facilitate comparability. These include the transfer learning models VGG16, RESNET50, and Densenet121. From the set of algorithms utilized in the suggested models, the resnet architecture obtains the highest accuracy of 90%.