ABSTRACT:

Vitamin deficiency can lead to various health issues, and early detection is crucial for timely intervention. This study presents a novel approach to detecting vitamin deficiency through image processing techniques combined with a Convolutional Neural Network (CNN). The proposed system analyzes facial and skin images to identify visual cues associated with deficiencies of vitamins such as A, B, C, and D. Using a dataset of medical images, the CNN model is trained to recognize features indicative of vitamin deficiency, such as skin discoloration, eye abnormalities, and other visible symptoms. The image processing techniques help enhance and segment the features, enabling the CNN to classify the deficiencies with high accuracy. The system aims to assist healthcare providers by offering a non-invasive and accessible tool for preliminary diagnosis. Future studies are expected to focus on refining the key characteristics linked to each deficiency, enhancing the system's accuracy and usability in clinical settings, and streamlining the diagnosis process for physicians. This approach can pave the way for a more efficient and ideal system for vitamin deficiency diagnosis.

Objective:
The primary objective of this project is to develop an image-based system for detecting vitamin deficiencies using Convolutional Neural Networks. The key goals include:
- Developing an accurate and efficient image processing pipeline to enhance features related to vitamin deficiencies.
- Training a CNN model to recognize and classify the signs of vitamin deficiencies from images.
- Creating a user-friendly system that can assist healthcare professionals in diagnosing vitamin deficiencies in patients without the need for invasive tests.
- Improving the diagnostic process by reducing time, cost, and complexity, making it more accessible in resource-limited settings.