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Recognition of food type and calorie estimation using neural network

Abstract

Across the globe, health cognizant among the people is increasing and everyone wants to maintain a healthy and normal life. But due to the fast moving world, obesity and other related issue becomes the major health problem among the human beings. According to medical experts, a person is defined as obese when their BMI is greater than 30 kg/m2. Obesity leads to many diseases like high cholesterol, liver failure, breathing issues, heart problems, diabetes and sometimes cancer. By eating healthy foods with high nutrition and low calorie values, we can control the obesity among the people. Human cannot control their appetite and have the nature of eating food which they like the most which leads to obesity. Many people have the difficulty in choosing the food items that have good nutrient and low calorific values. If a system can help the people and give them suggestions about the food and its calorific values, we can find a solution for this obesity problem. In this paper, identifying the food type and its calorific value estimation is done using multilayer perceptron model and the results are discussed. From the mixed food items, region of interest is selected from which the features are extracted. Extracted features are fed as the input to the MLP. Based on the food volume, the calories present in the food are calculated. Implementation of the algorithm is done in MATLAB environment for fruits and food items. The results showed that the level of detection of food item and accuracy of estimation of calorific level was acceptable.

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Acknowledgements

This work has supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (No. NRF-2019R1F1A1058715) and supported by the Chung-Ang University Research Scholarship Grants in 2020.

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Correspondence to Sanghyun Seo.

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Kumar, R.D., Julie, E.G., Robinson, Y.H. et al. Recognition of food type and calorie estimation using neural network. J Supercomput 77, 8172–8193 (2021). https://doi.org/10.1007/s11227-021-03622-w

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Keywords

  • BMI
  • ROI
  • Feature extraction
  • MLP