Vitamin Deficiency Detection Using Image Processing

Vitamin deficiencies are among the most common and overlooked health issues, often leading to severe long-term complications if not detected early. Traditional diagnosis methods rely heavily on clinical tests, which can be time-consuming, expensive, and inaccessible in rural or resource-limited areas. This project proposes an automated system for Vitamin Deficiency Detection using Image Processing to enable fast, reliable, and non-invasive screening. The method involves capturing facial images and applying preprocessing techniques such as noise removal, contrast enhancement, and region-of-interest extraction. Key visual biomarkers—including skin tone variation, eye discoloration, hair texture changes, and lip pigmentation— are analyzed using feature extraction techniques. A machine learning model is then used to classify the type of deficiency, focusing mainly on Vitamin A, B12, C, and D indicators. Experimental results demonstrate that the proposed system can achieve high accuracy in identifying deficiency patterns from simple images, making it a promising tool for early detection and preventive healthcare. This approach offers a cost-effective, portable, and scalable solution that can support clinical decision-making and improve public health outcomes.