Deep Learning Based Non-Invasive Framework for Nutritional Deficiency Detection Using Hair and Nail Images

The study presents a non-invasive framework using Convolutional Neural Networks (CNNs) to detect nutritional deficiencies through hair and nail images, achieving an 89% accuracy rate. This method offers a cost-effective alternative to traditional blood tests, particularly benefiting women, children, and socioeconomically disadvantaged groups. By analyzing high-resolution images of size 224 × 224 pixels and employing data augmentation, the model can identify deficiencies in iron, zinc, biotin, and vitamins, facilitating early diagnosis and improving healthcare accessibility.