Food Calorie Recommender System Using Mobilenet Deep Learning Technique - T. Roopika; S. Muni Kumar (International Journal of Scientific Research in Science, Engineering and Technology, 2025)
Food image classification has gained significant attention in recent years due to its relevance in health monitoring, dietary assessment, and medical applications. Automated recognition of food items from images can assist in calorie estimation, nutrition tracking, and disease-specific diet planning. This study presents a deep learning-based approach using Convolutional Neural Networks (CNNs) for classifying food images. Two widely used models, SqueezeNet and VGG-16, are implemented and evaluated. Performance enhancement is achieved through data augmentation techniques and hyperparameter tuning. SqueezeNet, being lightweight and efficient, is ideal for real-time deployment on resource-constrained devices. Meanwhile, VGG-16, despite its larger size, demonstrates strong accuracy due to its deep architecture. Complex feature extraction further boosts classification performance in both models. Experimental results indicate that the proposed fine-tuned SqueezeNet achieved higher classification accuracy compared to the baseline VGG-16. These findings support the potential of deploying deep learning models for practical food image recognition in healthcare applications.