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

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Abstract

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.

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Data availability

Data and material is shared with a link and can be made available on request.

Notes

  1. https://www.image-net.org.

  2. https://economictimes.indiatimes.com/magazines/panache/this-dish-was-ordered-115-times-per-minute-samosa-crowned-as-most-binged-snack-of-the-year-reveals-swiggys-2021-data"/articleshow/88432318.cms.

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Authors and Affiliations

  1. School of Computer Science and Engineering, IILM University, Greater Noida, Uttar Pradesh, 201306, India

    Rajeev Tiwari

  2. Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh, 281001, India

    Gourav Bathla

  3. Department of Allied Sciences, School of Health Science and Technology, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, 248001, India

    Shuchi Upadhyay

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  1. Rajeev Tiwari
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  2. Gourav Bathla
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  3. Shuchi Upadhyay
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Contributions

RT contributed to conceptualization, problem formation, design, writing, guiding, reviews. GB contributed to conceptualization, coding, writing and reviews. SU contributed to conceptualization, writing and reviews.

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Correspondence to Gourav Bathla.

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Tiwari, R., Bathla, G. & Upadhyay, S. IndianFoodNet: effective Indian multi-food identification and recommendation for hypertensive patients using deep convolutional neural network. Neural Comput & Applic 36, 8625–8640 (2024). https://doi.org/10.1007/s00521-024-09537-w

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