Prediction For Melanoma Disease Using Deep Learning

Abstract

Skin cancer, including melanoma, is a serious global health concern. It is essential to recognize skin diseases early to improve treatment results. In this project, our goal is to develop a system that enhances the early detection of skin melanoma and other types of skin lesions using deep learning and computer vision techniques. Skin melanoma is a type of skin cancer that can be life-threatening if not diagnosed and treated promptly. The project aims to provide an effective solution for dermatologists, healthcare professionals, and individuals to identify potential skin melanoma through an intelligent system. Our system employs convolutional neural networks (CNN) models (EfficientNet-B0, Resnet50, Ensemble Model) designed for skin melanoma detection. Users can upload skin images to the web application, which is then fed to the backend CNN models for analysis. The model's predictions are presented to users through the web application interface, enabling early detection and informed decision-making. The significance of this project lies in its potential to aid medical professionals and individuals in identifying potential skin melanoma at an early stage, thus improving the chances of successful treatment. Early detection can make a substantial difference in patient outcomes. The developed system enhances skin melanoma detection by harnessing the power of deep learning and making it accessible to users through a user-friendly web application. This project serves as a valuable tool in the ongoing efforts to combat skin melanoma effectively.