Skin Disease Detection Using Ensemble Learning

Skin diseases affect millions of people worldwide, making early detection and accurate diagnosis crucial for effective treatment. However, the process of diagnosing skin conditions often requires specialized dermatological expertise, which can be time-consuming and expensive. To address this challenge, we have developed a web-based Skin Disease Detection System using cutting-edge machine learning and ensemble techniques. Our system leverages the HAM10000 dataset, consisting of 10,000 dermoscopic images representing seven common skin lesion types. We employ an ensemble learning approach, integrating ResNet50, EfficientNetB0, and DenseNet121 as base models and combining their predictions using a logistic regression meta-model. This architecture enhances the accuracy and robustness of the predictions. The backend of our application is built with Flask, responsible for model processing, authentication, and handling API requests. The React frontend provides an intuitive user interface where users can upload skin lesion images and receive real-time predictions along with confidence scores. The system also incorporates authentication and token verification for secure user access, ensuring data privacy and integrity. Our project aims to provide an accessible, reliable, and cost-effective tool to assist healthcare professionals and individuals in the early detection of skin diseases, potentially improving patient outcomes and reducing the burden on dermatology services