Vitamin Deficiency Detection System
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| Format: | Recurso digital |
| Langue: | anglais |
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Zenodo
2025
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| _version_ | 1866902295516545024 |
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| author | Janhavi Avinash Khune Saniya Ajay Shiradkar Tejas Pravin Admane Vidhi Dilip Kumar Nimje Dr. Nitin S. More |
| author_facet | Janhavi Avinash Khune Saniya Ajay Shiradkar Tejas Pravin Admane Vidhi Dilip Kumar Nimje Dr. Nitin S. More |
| contents | <p><span lang="EN">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.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15718974 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Vitamin Deficiency Detection System Janhavi Avinash Khune Saniya Ajay Shiradkar Tejas Pravin Admane Vidhi Dilip Kumar Nimje Dr. Nitin S. More CNN, Linear regression, HTML, CSS, JAVASCRIPT, OpenCV, Deep Learning, Flask <p><span lang="EN">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.</span></p> |
| title | Vitamin Deficiency Detection System |
| topic | CNN, Linear regression, HTML, CSS, JAVASCRIPT, OpenCV, Deep Learning, Flask |
| url | https://doi.org/10.5281/zenodo.15718974 |