A comprehensive approach to vitamin deficiency detection through image analysis of sense organs images using convolutional neural networks

Vitamin deficiencies can lead to severe health issues, emphasizing the need for timely detection and intervention. This research introduces a deep learning-based solution to accurately detect vitamin deficiencies by analyzing images of body parts such as the tongue, nails, and skin. The system takes a hybrid approach, leveraging the capabilities of Convolutional Neural Networks (CNNs) to conduct local extraction of features and Vision Transformers (ViTs) for collecting the global patterns and relationships. High-quality images are preprocessed to enhance clarity and reduce noise before being analyzed by the model. A large and diverse data set is used to train the system, ensuring its ability to recognize various vitamin deficiency indicators. By leveraging advanced image processing and deep learning techniques, this research contributes to improving public health outcomes. The system’s efficiency and reliability make it a promising tool for healthcare applications.