AI based System for Vitamin Deficiency Detection

Vitamin deficiencies are a worldwide health problem that can result in very serious medical conditions if not diagnosed and treated. Detecting early and providing treatment can prevent long term problems. In this research, we present a novel, efficient system to detect vitamin deficiencies early based on physical symptoms using machine learning and computer vision. The proposed system uses advanced image processing to analyze the visual manifestations (skin anomalies, etc.) of deficiencies in vitamins D, A, and B complexes. A large dataset is used to train the machine learning model to achieve accuracy and reliability, which is enhanced through data augmentation, hyperparameter tuning, and thorough performance evaluation. A user-friendly interface built with Flask is also added to the system to allow for easy uploading, processing and categorization into specific deficiency categories and recommendations for further medical consultation. This research integrates modern engineering tools with healthcare applications to demonstrate the potential of artificial intelligence to improve diagnostic accuracy and accessibility, an important global health challenge.