IndianFoodNet: effective Indian multi-food identification and recommendation for hypertensive patients using deep convolutional neural network

• Published in: Neural computing & applications 2024-05, Vol.36 (15), p.8625-8640
• Main Authors: Tiwari, Rajeev, Bathla, Gourav, Upadhyay, Shuchi
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Artificial neural networks; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Datasets; Dietitians; Eating; Food; Hypertension; Image Processing and Computer Vision; Image segmentation; Meals; Medical imaging; Neural networks; Original Article; Probability and Statistics in Computer Science
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-024-09537-w

The food consumption has a direct effect on the health of an individual. Eating food without awareness of its ingredients may result in eating style-based diseases such as hypertension, diabetes, and several others. As per recent WHO survey, the number of persons with hypertension is very large in numbers. There is essentially a need of novel technique that can provide food recommendation to hypertensive persons, out of their multi-food items in their meals. In this research work, Indian multi-food items of the meal are recognized using fine-tuned deep convolutional neural network model. Further, in existing research works, only single food image is recognized, which is not relevant to real-life food consumption. In our proposed approach, contour-based image segmentation technique is used for multi-food meal. In existing research works, no dataset is available on Indian food items for hypertensive persons. The key contribution of this research work is the preparation of Indian food dataset of 30 classes for hypertensive patients. There are 15 Recommended food classes for the hypertensive person and 15 classes are not recommended foods to maintain the class balance (as calibrated through a professional dietitian) (Dr. Shuchi Upadhyay, Dietitian and Nutrition expert, UPES, Dehradun). The novel contribution is to present ‘IndianFood30’ dataset of hypertensive patients for research purposes. Further, a novel IndianFoodNet model is presented which is trained on these 30 Indian food classes. Several pre-trained models are available for research purposes, but there is no pre-trained model on Indian food for hypertensive persons. Food ingradients exhibit high intra-class variance, and these complex features are extracted using our proposed approach. The accuracy of the proposed approach is compared with state-of-the-art models such as VGGNet, Inception V3, GoogleNet, and ResNet. Our proposed approach is also compared with some recent techniques on some of the existing datasets such as UEC Food-100, UEC Food-256, and Food-101 datasets to show the performance and effectiveness of the proposed model. Experiment analysis validates that our proposed approach outperforms existing approaches significantly.