Vitamin Deficiency Identification using Image Processing

Vitamin deficiency significantly impacts overall health, leading to anemia, complications during pregnancy or Vitamin deficiencies are a significant public health concern worldwide, particularly in resource-constrained settings. Early detection and diagnosis of vitamin deficiencies are crucial for timely intervention and prevention of long-term health consequences. Traditional methods of diagnosis is time-consuming and more costly which includes blood tests. Recent advancements in image processing and computer vision gives cost-effective screening of vitamin deficiencies childbirth, and impaired cognitive and physical development. It is essential to detect and treat such deficiencies to avoid adverse effects. Traditionally, vitamin deficiency is identified through blood tests, which is costly and takes time also. Consequently, many individuals neglect their vitamin health. Our objective is to provide a time-efficient and cost-free method to check for vitamin deficiencies at any time. Vitamin deficiencies show visible impacts on organs such as the eyes, nails, lips, tongue, and skin. We aim to identify these impacts and determine specific vitamin deficiencies through images of various organs using advanced image processing techniques. We use sophisticated deep learning techniques and machine learning algorithms to identify vitamin deficiency efficiently. After identification of deficiency, our system will suggest dietary recommendations to address the issue. If the deficiency is severe, it will also advise consulting a doctor. Hence, we propose a system to facilitate early diagnosis of vitamin deficiencies, saving time and costs and making it convenient for users.