Authors:
B. Shilpa Shree, D. Sumathi, Prem Kumar Ramesh, M. Sindhu, S. P. Indushri, S. Naga Lahari, Farrukh Arslan

Addresses:
Department of Computer Science and Engineering, CMR Institute of Technology, Bengaluru, Karnataka, India. Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America.

Abstract:

Nutritional inadequacies are a big worry these days. Iron, zinc, biotin, and other vitamin deficiencies can cause a range of health problems, including anemia, a weakened immune system, hair loss, and nails that aren't growing properly. The problems that were talked about above affect everyone, but they are especially bad for women, children, and those who come from less fortunate socioeconomic backgrounds. The most common way to detect deficiencies is through expensive blood tests, which makes it hard for people to get tested regularly. This research proposes a pragmatic methodology using Convolutional Neural Networks (CNNs) to precisely detect deficiencies in hair and nails by analyzing high-resolution images. Our method uses a CNN trained on images of hair and nails. It has an 89% accuracy rate in distinguishing between different types of deficiencies, including iron, zinc, biotin, and vitamin deficiencies. The model uses several convolutional layers to process images of size 224 × 224 pixels. It also uses data augmentation to improve its accuracy. This technique also helps people in communities that aren't getting enough aid get an early diagnosis at a low cost. 

Keywords: Convolutional Neural Networks (CNNs); Nutritional Deficiency; Hair and Nail Analysis; Medical Imaging; Healthcare Screening; Vitamin Detection; Traditional Diagnosis; Health Problems.

Received on: 03/04/2025, Revised on: 08/06/2025, Accepted on: 01/09/2025, Published on: 08/03/2026

DOI: 10.69888/FTSHSL.2026.000595

FMDB Transactions on Sustainable Health Science Letters, 2026 Vol. 4 No. 1, Pages: 73–81

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