CNN-based diagnostic system for diabetic foot ulcer analysis

Diabetic Foot Ulcer (DFU) is a common and severe complication in diabetic patients that can lead to infections, amputations, and even mortality if not detected and managed promptly. This project presents an intelligent system for the early detection of diabetic foot ulcers using machine learning and image processing techniques, integrated with a user-friendly interface that enhances patient support. The system uses a convolutional neural network (CNN) to analyze foot images and accurately classify them into ulcerated and non-ulcerated categories. Alongside detection, the application provides personalized diet plans and medical suggestions tailored to the user’s condition. These recommendations are designed to help users manage their blood sugar levels and promote faster wound healing. The user interface is designed to be intuitive, allowing patients to upload images, view results, and receive actionable advice in real-time. This holistic approach not only aids early diagnosis but also supports ongoing care and prevention. The solution is particularly valuable in remote or underserved areas where access to specialists is limited. With the potential for mobile integration and real-time monitoring, this system demonstrates how artificial intelligence can play a vital role in improving diabetic care and reducing the risks associated with DFU.