Nutri Scan AI System Using Image Processing and Neural Networks

Vitamin deficiencies result in physical health problems which attack nails and tongues and skins of people globally. When medical practitioners identify vitamin deficiencies, they should activate early diagnostic methods to enable adequate preventive actions and treatment approaches. A machine learning system uses pictures of nail and tongue and skin defects to forecast vitamin deficiency cases. This system evaluates anomalies through neural network analysis of Convolutional Neural Networks (CNN) along with MobileNet and Long Short-Term Memory (LSTM) networks that function across various deep learning depth levels at the top position where MobileNet serves as the hybrid model. The project consists mainly of an analysis component for nail and tongue pictures alongside a classification part for skin problems. The first part of the system applies SVM to work with CNN and MobileNet to spot Acral Lentiginous Melanoma Beaus Line and Koilonychia because these symptoms show different vitamin deficiencies. This module classifies skin images to identify health conditions which include Actinic Keratosis together with Basal Cell Carcinoma and Melanoma because these conditions present as vitamin deficiency symptoms. The essential part of this project involves creating a web-based application through Flask for users to submit images of their nails and skin and tongue for predictive analysis. The web application generates custom vitamin guidance which it defines through its detection of health concerns as well as dietary instructions to compensate for nutrient inadequacies. Performance evaluation of the system requires precision and recall measurement and confusion matrix analysis. The machine learning models acquire training from various datasets through evaluation procedures to develop accuracy in their prediction abilities. The development of an efficient health diagnostic tool for early vitamin deficiency recognition relies on modern machine learning techniques to fulfill healthcare prevention and management needs.