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 deficiencies through image processing techniques combined with a Convolutional Neural Network (CNN). The proposed system consists of a user-friendly frontend interface that allows users to upload facial and skin images, which are then analyzed to identify visual cues associated with deficiencies of essential vitamins such as A, B, C, and D.
Using a dataset of medical images, the CNN model is trained to recognize specific patterns indicative of vitamin deficiencies, such as skin discoloration, eye abnormalities, and other visible symptoms. Advanced image processing techniques are used to read, enhance, and segment the uploaded images, enabling the CNN to classify the deficiencies with high accuracy.
Vitamin deficiency is a widespread health issue that significantly impacts the quality of life for millions of people worldwide. Many health problems, ranging from mild symptoms to severe medical conditions, can arise due to the lack of essential vitamins and minerals. Despite the availability of resources, it remains difficult for individuals to track and meet their nutritional needs accurately, especially without medical consultation. A significant portion of the global population is unknowingly suffering from various vitamin deficiencies. Over 2 billion people are affected globally, with deficiencies in essential nutrients such as zinc, iron, and vitamin D contributing to severe health issues.
Software Requirement:
1. HTML
2. CSS
3. JavaScript
4. Python
5. Flask
6. MySQL
7. XAMPP
8. VS Code
9. Bootstrap
Hardware Requirement:
1. PC or Laptop
2. 500GB HDD (Hard Disk Drive) with 4GB+ RAM
3. Keyboard and Mouse
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