Smart Detection of Vitamin Deficiencies: Leveraging CNN and Image Processing for Precise Diagnosis

Vitamin deficiency has become a prevalent global public health concern, especially in medical centers. Moreover, the growing prevalence has major implications for disease severity, mortality, and morbidity. Because of certain drawbacks that exist concerning cost and time in conventional diagnostic techniques, interest has shifted towards automated processes for correct prediction and cost-effectiveness in diagnosing Vitamin deficiency. The revised abstract succinctly summarizes the objectives, innovative methodology, and key findings of this research effort and holds great potential to revolutionize the diagnostic aspects of medicine in terms of efficiency as well as accuracy. This program addresses a major worldwide health issue that affects millions owing to a lack of dietary understanding. In the long run, it will allow healthcare staff to make more accurate diagnoses. The suggested method combines image-processing techniques with convolutional neural networks to provide early, non-invasive screening, individualized recommendations, and healthcare decision assistance. Future enhancements will involve dataset extension and more study into CNN architectures for improved accuracy and implementation.