Abstract
In the realm of nutrition science, it is well-recognized that individuals’ dietary needs vary based on factors such as age, gender, and health status. This divergence in nutritional requirements is particularly critical for vulnerable groups, including newborns, the elderly, and individuals with diabetes, as their dietary choices can have profound implications for their health. Moreover, the dearth of Uzbek recipes in mainstream culinary literature, which predominantly focuses on Western cuisine, exacerbates the issue. To address these challenges, this study undertakes the ambitious task of constructing a comprehensive AI system, comprising both backend and frontend components, tailored to the nuances of Uzbek cuisine. The primary objectives encompass recipe classification, ingredient identification and localization, and the estimation of nutritional content and calorie counts for each dish. Convolutional Neural Networks (CNNs) are employed as the cornerstone of this computational solution, proficiently handling image-based tasks, including the recognition of diverse food items and portion size determination within Uzbek recipes. Food classification is executed using MobileNet, while the You-Only-Look-Once (YOLO) network plays a pivotal role in the dual functions of ingredient classification and localization within dishes. Upon rigorous training, testing, and system deployment, users can effortlessly capture images of food items through the smartphone application, facilitating the estimation of nutritional data and calorie counts. Ultimately, this vital information is presented to users via the smartphone interface, bridging the accessibility gap and enhancing comprehension of nutritional aspects within Uzbek cuisine.
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References
World Health Organization: Obesity and Overweight (2018)
NIPH: Overweight and obesity in Norway. Tech. rep. (2014)
Mukhiddinov, M., Djuraev, O., Akhmedov, F., Mukhamadiyev, A., Cho, J.: Masked face emotion recognition based on facial landmarks and deep learning approaches for visually impaired people. Sensors 23(3), 1080 (2023)
Mukhiddinov, M., Jeong, R.G., Cho, J.: Saliency cuts: salient region extraction based on local adaptive thresholding for image information recognition of the visually impaired. Int. Arab J. Inf. Technol. 17(5), 713–720 (2020)
Mukhiddinov, M., Akmuradov, B., Djuraev, O.: Robust text recognition for Uzbek language in natural scene images. In: 2019 International Conference on Information Science and Communications Technologies (ICISCT), pp. 1–5. IEEE. (2019)
Sathish, S., Ashwin, S., Quadir, M.A., Pavithra, L.K.: Analysis of convolutional neural networks on indian food detection and estimation of calories. In: Materials Today: Proceedings, 16 Mar (2022)
Li, S., Zhao, Y., Liu, S.: How food shape influences calorie content estimation: the biasing estimation of calories. J. Food Qual. 24 May (2022)
Kumar, R.D., Julie, E.G., Robinson, Y.H., Vimal, S., Seo, S.: Recognition of food type and calorie estimation using neural network. J. Supercomput. 77(8), 8172–8193 (2021)
Bossard, L., Guillaumin, M., Gool, L.V.: Food-101–mining discriminative components with random forests. In: European Conference on Computer Vision, pp. 446–461. Springer, Cham (2014)
Keras: Resnet-50 [Online]. Available: https://www.kaggle.com/keras/resnet50 (2017).
Rakhmatillaevich, K.U., Ugli, M.M.N., Ugli, M.A.O., Nuruddinovich, D.O.: A novel method for extracting text from natural scene images and TTS. Eur. Sci. Rev. 1(11–12), 30–33 (2018)
Mukhamadiyev, A., Mukhiddinov, M., Khujayarov, I., Ochilov, M., Cho, J.: Development of language models for continuous Uzbek speech recognition system. Sensors 23(3), 1145 (2023)
Abdusalomov, A.B., Mukhiddinov, M., Whangbo, T.K.: Brain tumor detection based on deep learning approaches and magnetic resonance imaging. Cancers 15(16), 4172 (2023)
Khamdamov, U., Abdullayev, A., Mukhiddinov, M., Xalilov, S.: Algorithms of multidimensional signals processing based on cubic basis splines for information systems and processes. J. Appl. Sci. Eng. 24(2), 141–150 (2021)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Ege, T., Yanai, K.: Image-based food calorie estimation using knowledge on food categories, ingredients and cooking directions. In: Proceedings on Thematic Workshops of ACM Multimedia, pp. 367–375 (2017)
Mukhiddinov, M., Abdusalomov, A.B., Cho, J.: Automatic fire detection and notification system based on improved YOLOv4 for the blind and visually impaired. Sensors 22(9), 3307 (2022)
Mukhiddinov, M., Cho, J.: Smart glass system using deep learning for the blind and visually impaired. Electronics 10(22), 2756 (2021)
Jalal, M., Wang, K., Jefferson, S., Zheng, Y., Nsoesie, E.O., Betke, M.: Scraping social media photos posted in Kenya and elsewhere to detect and analyze food types. In: Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management, pp. 50–59 (2019)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, LC.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4510–4520 (2018)
Wu, Z., Shen, C., Van Den Hengel, A.: Wider or deeper: revisiting the resnet model for visual recognition. Pattern Recogn. 90, 119–133 (2019)
Koonce, B.: EfficientNet. In: Convolutional Neural Networks with Swift for Tensorflow, pp. 109–123. Apress, Berkeley, CA (2021)
Yuldashev, Y., Mukhiddinov, M., Abdusalomov, A.B., Nasimov, R., Cho, J.: Parking lot occupancy detection with improved mobilenetv3. Sensors 23(17), 7642 (2023)
Abdusalomov, A., Mukhiddinov, M., Djuraev, O., Khamdamov, U., Whangbo, T.K.: Automatic salient object extraction based on locally adaptive thresholding to generate tactile graphics. Appl. Sci. 10(10), 3350 (2020)
Chen, G., et al.: Food/non-food classification of real-life egocentric images in low-and middle-income countries based on image tagging features. Front. Artif. Intell. 4, 644712 (2021)
Mukhiddinov, M.: November. Scene text detection and localization using fully convolutional network. In: 2019 International Conference on Information Science and Communications Technologies, pp. 1–5 (2019)
Khamdamov, U.R., Mukhiddinov, M.N., Djuraev, O.N.: An overview of deep learning-based text spotting in natural scene images. Problems of Computational and Applied Mathematics. Tashkent, 2(20), 126–134 (2020)
Muminov, A., Mukhiddinov, M., Cho, J.: Enhanced classification of dog activities with quaternion-based fusion approach on high-dimensional raw data from wearable sensors. Sensors 22(23), 9471 (2022)
Farkhod, A., Abdusalomov, A.B., Mukhiddinov, M., Cho, Y.I.: Development of real-time landmark-based emotion recognition CNN for masked faces. Sensors 22(22), 8704 (2022)
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Abdusalomov, A., Mukhiddinov, M., Djuraev, O., Khamdamov, U., Abdullaev, U. (2024). AI-Based Estimation from Images of Food Portion Size and Calories for Healthcare Systems. In: Choi, B.J., Singh, D., Tiwary, U.S., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2023. Lecture Notes in Computer Science, vol 14532. Springer, Cham. https://doi.org/10.1007/978-3-031-53830-8_2
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