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Vitamin Deficiency Detection using Real Time Image and Neural Network

  • Unique Paper ID: 163520
  • Volume: 10
  • Issue: 11
  • PageNo: 2189-2192
  • Abstract:
  • Vitamin deficiencies remain a global health concern, contributing to various adverse health outcomes. Early detection and intervention are crucial in mitigating the associated risks. This proposes a novel approach for detecting vitamin deficiencies leveraging real-time sample images and neural network techniques. The proposed system utilizes a dataset comprising images of individuals exhibiting symptoms associated with different vitamin deficiencies. These images, captured in real-time, are preprocessed to extract relevant features and fed into a convolutional neural network (CNN) for classification. The CNN is trained to recognize patterns indicative of specific deficiencies, enabling accurate diagnosis in real-time. The efficacy of the proposed method is evaluated through extensive experimentation, achieving promising results in terms of accuracy and efficiency. Furthermore, the system's potential for integration into existing healthcare frameworks is discussed, emphasizing its utility in facilitating early diagnosis and personalized interventions for individuals at risk of vitamin deficiencies. Overall, this presents a valuable contribution to the field of preventive healthcare, offering a technologically advanced solution for addressing the challenges associated with vitamin deficiency detection.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 11
  • PageNo: 2189-2192

Vitamin Deficiency Detection using Real Time Image and Neural Network

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Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

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