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@article{168098,
        author = {Kundan Prasad and Dr. A. Sasi Kumar},
        title = {AlexNet-Based Detection of Vitamin Deficiency in Humans},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {4},
        pages = {1268-1274},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168098},
        abstract = {This paper introduces a novel, cost-effective Artificial Intelligence (AI) application designed to detect vitamin deficiencies in humans using images of specific body organs. Unlike current methods that rely on expensive laboratory analysis, this application allows individuals to diagnose potential deficiencies by analyzing photos of their eyes, lips, and tongue, eliminating the need for blood samples. The software is trained to accurately identify deficiencies by analyzing changes in tissue structure associated with nutritional deficits in these body parts. Additionally, it provides users with nutritional recommendations to address detected deficiencies and prevent associated complications. Medical professionals can contribute to improving the application's accuracy by providing and verifying patient data, enhancing its diagnostic capabilities beyond human ability. This tool addresses a global issue stemming from insufficient nutritional awareness and will aid healthcare workers in achieving more precise diagnoses over time. This research proposes an intelligent system utilizing deep learning techniques to detect and differentiate vitamin deficiencies from human tissue. Through image clustering and segmentation, the effectiveness of the proposed technique is evaluated, suitable features are extracted, and classification results are compared with other methods.},
        keywords = {Deep Learning, Human Tissue Identification, Convolutional Neural Network, Pre trained Models, Image Processing.},
        month = {September},
        }