REAL-TIME FOOD CLASSIFICATION USING VGG19 WITH GRAD-CAM VISUALIZATION FOR ENHANCED INTERPRETABILITY

As nutritional disorders increase, because of unbalanced diets and numerous cuisines available on markets, physical and mental health of humans is more likely to be ruined. Appropriate food categorization is essential in tracking food consumption and sensitizing nutrition. The following paper proposes a food recognition model based on the VGG19 deep convolutional neural network with the incorporated Grad-CAM (Gradient-weighted Class Activation Mapping) visualization of the predictions made by the model to improve its interpretability. The model is tested against extensive dataset of 53 dishes in international cuisine on which both non-vegetarian and vegetarian food items are found. VGG19 is chosen because it performs well in feature detection and Grad-CAM gives the areas on each image that affect the decisions made by the model, making them more transparent, and trustworthy. This explains AI model enables users and researchers to visualize and check classification results. As measured by experimental analysis, the enhanced VGG19 classifier with Grad-CAM achieves an efficient accuracy of 96 percent, which is better compared to the traditional machine learning models including Naive Bayes and Decision Tree. This proposed approach is effective in real-time applications monitoring food intake and diet assessment.