Detection of Vitamin Deficiencies Using Convolutional Neural Networks: A Novel Approach

@article{1707545,
      author = {Sri Vishva Mohan K G, Siddaram Baburao Madabhavi, Sandeep Dhimal, Vijendra S N},
      title = {Detection of Vitamin Deficiencies Using Convolutional Neural Networks: A Novel Approach},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {9},
      pages = {865-868},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707545.pdf},
      abstract = {Vitamin deficiencies pose a significant global health challenge, often leading to severe medical conditions if left undetected. Early diagnosis is crucial for effective treatment, but conventional detection methods can be time-consuming and resource-intensive. This study presents an advanced deep learning-based model utilizing Alex Net for automated vitamin deficiency detection. The proposed system analyzes medical images and extracts relevant features to classify deficiencies associated with vitamins A, B, C, and D. The model is trained on a curated dataset, achieving high accuracy in detecting deficiency patterns. Extensive experimentation demonstrates the model's effectiveness in identifying deficiencies with improved precision compared to traditional diagnostic approaches. The findings suggest that deep learning can be a valuable tool in medical diagnostics, providing a scalable, cost-effective, and non-invasive solution for early vitamin deficiency detection.},
      keywords = {deep learning features; Medical Image Analysis; Alex Net; CNN features.},
      month = {March},
  }