Deep-learning-based food calorie measurement with graphical user interface

Ch. Sarada; S. Suguna Mallika; G. Swetha

doi:10.1117/12.3030407

26 June 2024 Deep-learning-based food calorie measurement with graphical user interface

Ch. Sarada, S. Suguna Mallika, G. Swetha

Proceedings Volume 13188, International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors (MIEITS 2024); 1318804 (2024) https://doi.org/10.1117/12.3030407
Event: International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors (MIEITS 2024), 2024, Kuala Lumpur, Malaysia

Abstract

People worldwide are becoming increasingly conscious about leading a healthy life. However, in the current busy day to day regimes, paucity of time to properly consult nutritionist for a customized food chart is certainly difficult for a lot of people due to which their health takes a back seat. Thus, a trustworthy technique for calorie and nutrient sustenance estimate in food is needed. The patient must record their daily food intake for monitoring, however due to factors including lack of nutrition, knowledge, or self-discipline, it is difficult for patients to analyze or regulate their daily intake. The proposed approach helps dieticians and patients calculate their daily calorie intake. It is a web-based approach employing computer vision for estimating fruit calories and vegetable calories that improves individual’s utilization propensities of wellness. The goal of the proposed work is to create an accurate and user-friendly calorie measurement system that can help individuals make more informed dietary choices and monitor their calorie intake. The proposed system can accurately measure the calories in 95% cases.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ch. Sarada, S. Suguna Mallika, and G. Swetha "Deep-learning-based food calorie measurement with graphical user interface", Proc. SPIE 13188, International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors (MIEITS 2024), 1318804 (26 June 2024); https://doi.org/10.1117/12.3030407

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