VITAMIN DEFICIENCY DETECTION USING DEEP LEARNING

Registration ID: IJNRD_306933

Published ID: IJNRD2506058

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K.TULASI KRISHNA KUMAR, PEELA VENKAT VINEETH

Vitamin deficiency, deep learning, CNN, OpenCV, Introduction, Pre-processing, Visually noticeable symptoms, Diagnosis, Model, Dataset.

Vitamin deficiencies can manifest in a variety of ways across different regions of the human body, often producing visible symptoms that may serve as early indicators of underlying nutritional issues. These signs can include changes in the appearance of the eyes, discoloration or dryness of the lips, alterations in the texture or color of the tongue, and abnormalities in the nails. Traditionally, by identifying specific vitamin deficiencies requires blood tests and laboratory analysis, which can be time-consuming, costly, and inaccessible to many people. To address this challenge, our application leverages computer vision and artificial intelligence to provide a non-invasive method for identifying potential vitamin deficiencies. By simply analyzing photographs of key areas such as the eyes, lips, tongue, and nails, the system can offer preliminary assessments without the need for clinical testing. The core of this application is a deep learning-based on the Convolutional Neural Network (CNN), which has been trained to recognize patterns and features associated with various deficiencies. In the development of this project, we compiled a comprehensive dataset consisting of labeled images which are focused on the afore mentioned body parts. Prior to training, these images undergo a series of pre-processing steps, which includes resizing, normalization, and augmentation, to improve the robustness and accuracy of the model. The pre-processed data is then used to train the CNN, enabling it to learn distinguishing features and make accurate predictions. After the training phase is completed, the model is saved and subsequently evaluated using the OpenCV library for real-time image processing and testing. This allows the users to interact with the application in a practical setting, either through uploaded images or live camera input, to receive insights into potential vitamin-related health concerns. The ultimate goal of this project is to provide a user-friendly, accessible tool that supports for the early detection and promotes better nutritional awareness.

Published Paper   E-Certificate

K.TULASI KRISHNA KUMAR & PEELA VENKAT VINEETH (June-2025). VITAMIN DEFICIENCY DETECTION USING DEEP LEARNING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(6), a557-a564. https://ijnrd.org/papers/IJNRD2506058.pdf

Volume 10 Issue 6, June-2025

Pages : a557-a564

Paper Reg. ID: IJNRD_306933

Published Paper Id: IJNRD2506058

Downloads: 000122004

Research Area: Science All

Author Type: Indian Author

Country: Visakhapatnam, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2506058.pdf

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