FoodSynthNet: Generative AI Framework for Creating Class Balanced Food Image Datasets and Intelligent Classification for Nutrition Monitoring and Food Recognition Applications

Abstract

FoodSynthNet is a Generative AI–based framework developed for intelligent food recognition and nutrition monitoring. The system employs Conditional Generative Adversarial Networks (cGANs) to generate class-balanced food image datasets, effectively addressing class imbalance and improving classification accuracy. Users can securely upload food images through a web-based interface, where the system identifies the food item and analyzes its nutritional content. Detailed information such as calories, nutrients, health benefits, disadvantages, and dietary recommendations is provided. By integrating food recognition with nutrition analysis, FoodSynthNet supports nutrition awareness, healthy eating habits, and preventive healthcare applications.