Vitamin Deficiency Detection Using Image Processing and Convolutional Neural Networks (CNN)

Vitamin Deficiency Detection Using Image Processing and Convolutional Neural Networks (CNN) is an advanced AI-based healthcare project developed for final year engineering students. The system analyses facial, eye, lip, tongue, skin, and nail images to identify potential vitamin deficiencies using deep learning and computer vision techniques. Traditional vitamin deficiency diagnosis often requires laboratory tests, which can be time-consuming and expensive. This project provides a non-invasive and intelligent alternative by processing images through a trained CNN model to detect visible symptoms associated with common vitamin deficiencies. The application performs image preprocessing, feature extraction, classification, and prediction to determine possible deficiencies such as Vitamin A, Vitamin B Complex, Vitamin C, Vitamin D, Vitamin E, and Vitamin K. The system also displays prediction confidence, recommended nutritional guidance, and historical reports. The project is designed with a user-friendly interface and can be extended for hospitals, healthcare centres, telemedicine platforms, and mobile health applications. Key Highlights: • AI-powered healthcare diagnosis • Deep Learning using CNN • Image Processing techniques • High prediction accuracy • Easy-to-use interface • Fast and non-invasive detection • Suitable for real-world healthcare applications This project is ideal for Computer Science, Artificial Intelligence, Machine Learning, Information Technology, Biomedical Engineering, and Data Science students seeking an innovative healthcare-based final year project.