Deep learning-based skin lesion classification for cancer detection

Abstract Early detection is crucial for a successful remedy of skin cancer and an upsurge of longevity, as it is one of the most ubiquitous kinds of cancer globally. This project introduces a skin cancer detection system that employs Convolutional Neural Networks (CNN) to autonomously classify skin lesions into distinct categories based on image inputs. The system employs deep learning techniques to analyse images and forecast the presence of several types of malignancy for the skin, including malignancies of basal cells and melanoma. The algorithm was honed using a dataset of tagged skin lesion pictures, incorporating advanced techniques such as Batch Normalization and Max Pooling to prevent overfitting and improve accuracy. In order to minimize overfitting, dropout layers were included, and the architecture was improved to optimize the model's functionality. The prediction findings are later displayed through a web-based interface that was constructed using Flask. Users are able to submit skin pictures and receive thorough findings, which include information about the expected cancer type. The objective of this system is to facilitate the diagnosis of skin cancer more efficiently for dermatologists and healthcare professionals by offering automated, high-accuracy predictions. The system may be expanded to include a mobile application in the future, which would provide a wider range of accessibility for the rapid identification of malignancy.