Vol. 15 No. 2 (2025): IJCRT, Volume 15, Issue 2, 2025
Journal Article

Vitamin Deficiency Detection System

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  • Janhavi Avinash Khune,
  • Saniya Ajay Shiradkar,
  • Tejas Pravin Admane,
  • Vidhi Dilip Kumar Nimje,
  • Dr. Nitin S. More
Janhavi Avinash Khune
Department of Computer Science and Engineering, MIT Art Design and Technology University, Pune, India.
Saniya Ajay Shiradkar
Department of Computer Science and Engineering, MIT Art Design and Technology University, Pune, India.
Tejas Pravin Admane
Department of Computer Science and Engineering, MIT Art Design and Technology University, Pune, India.
Vidhi Dilip Kumar Nimje
Department of Computer Science and Engineering, MIT Art Design and Technology University, Pune, India.
Dr. Nitin S. More
Department of Computer Science and Engineering, MIT Art Design and Technology University, Pune, India.
DOI: https://doi.org/10.5281/zenodo.15718974
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Published 2025-06-23

How to Cite

Janhavi Avinash Khune, Saniya Ajay Shiradkar, Tejas Pravin Admane, Vidhi Dilip Kumar Nimje, & Dr. Nitin S. More. (2025). Vitamin Deficiency Detection System. IJCRT Research Journal | UGC Approved and UGC Care Journal | Scopus Indexed Journal Norms, 15(2), 50891–50898. https://doi.org/10.5281/zenodo.15718974

Copyright (c) 2025 IJCRT Research Journal | UGC Approved and UGC Care Journal | Scopus Indexed Journal Norms

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Vitamin deficiency has become a rampant worldwide health problem, associated with life-threatening complications like cardiovascular conditions, cancer, and immune disease. Conventional diagnosis is costly, invasive, and needs the expertise of the diagnostician. This paper presents a new, automated vitamin deficiency diagnostic system utilizing image processing and deep learning technology. Our method employs a CNN model trained from a database of annotated facial, skin, nail, and eye images to identify indicators of deficiencies. A minimalist web app permits users to upload images and provide real-time diagnostic feedback. The solution is inexpensive, scalable, and available, with controlled trials. The findings confirm the capability of AI-based image diagnosis as an addition to conventional methods and an advancement in accessible preventive healthcare.

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