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@article{174204,
        author = {N.Madhu Latha and A. Leela and N.Naga Navya Sri and S.sanyasi naidu and Mr. N M Ramalingeswararao},
        title = {Neural Network-Based Image Processing for Vitamin Deficiency Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3291-3299},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174204},
        abstract = {A significant amount of work has been done recently regarding CNN’s application in detecting Vitamin deficiencies using images. It begins with a code that lets the user select a body part that they would like to use for analysis tongue, lips, nails, or eyes. After selecting an image of the relevant body part, the image is pre-processed for features and quality improvement. The pre-processed images are then used to train the CNN with sufficient layers and training for specific deficiency signs. For example, if the tongue is selected, a CNN can classify symptoms such as smooth texture and red colour of the tongue, glossitis, or unknown mouth. If lips are chosen, the classifications may be cracked lips with a shiny red extremely pale in color and other associated symptoms. The deficiencies get detected on an image basis enabling prompt diagnosis and treatment. By implementing deep learning, the achievement in accuracy as well as automating the process of Vitamin deficiency detection shows the capabilities of CNNs in medical image analysis and proactive medicine.},
        keywords = {Vitamin Deficiency, Related Dataset, Deep Learning, Convolution Neural Network, Image Processing Techniques, Accuracy.},
        month = {March},
        }