Vitamin Deficiency Detection Using Machine Learning Through Image Processing and Symptom Analysis

Abstract: This paper presents a machine learning -based system for detecting vitamin deficiencies by combining facial image analysis with symptom -based evaluation. Using a Convolutional Neural Network (CNN) trained on a curated dataset of facial images labeled with various vitamin deficiencies, the model predicts the likelihood of deficiency with high accuracy. The system further refines its prediction by incorporating results from a symptom questionnaire, providing a final deficiency classification along with a confidence score. Additionally, th e application generates a comprehensive PDF report containing the detection results, annotated facial images, a deficiency probability graph, and dietary recommendations. The proposed solution is implemented in Google Colab, integrating the trained model with an interactive interface for image upload, symptom entry, real -time prediction, and report generation.

Keywords: Vitamin Deficiency Detection, CNN -Based Image Classification, Medical Image Analysis, Symptom -Based Diagnosis, Deep Learning for Healthcare, Convolutional Neural Network, Image and Symptom Integration.