Vitamin Deficiency Detection Using Image Processing and Neural Network

Abstract

Vitamin deficiencies are a significant global health concern with severe health consequences. This study proposes a novel approach for detecting vitamin deficiencies using image processing techniques and Convolutional Neural Networks (CNNs). The method involves image acquisition, preprocessing, feature extraction using a pre-trained CNN, and classification based on learned patterns. Extensive experiments on a diverse dataset demonstrate high accuracy in detecting various types of deficiencies. The proposed system offers non-invasive early screening, personalized recommendations, and healthcare decision support. The results highlight the potential for widespread implementation and future improvements in dataset expansion and CNN architecture exploration.