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Recognition of food type and calorie estimation using neural network

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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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References

  1. Blunt J, Morris J, Trigg J (2020) Diet and physical activity practices of South Australian adolescents. Heliyon 6:e04326

    Article  Google Scholar 

  2. Pouladzadeh P, Shirmohammadi S, Almaghrabi R (2014) Measuring calorie and nutrition from food image. IEEE Trans InstrumMeas 63:1947–1956

    Article  Google Scholar 

  3. Rajeswari M, Satheesh Kumar R, Subramanian C, Xavi A, Golden Julie E, Harold Robinson Y (2020) Person identification with aerial imaginary using SegNet based semantic segmentation. Earth Sci Inform 13:1293–1304

    Article  Google Scholar 

  4. Ahmed Subhi M, Hamid Ali S, Abulameer Mohammed M (2019) Vision-based approaches for automatic food recognition and dietary assessment: a survey. IEEE Access 7:35370–35381

    Article  Google Scholar 

  5. Torres JN, Mora M, García RH, Barrientos RJ, Fredes C, Valenzuela A (2020) A review of convolutional neural network applied to fruit image processing. MPDI J ApplSci 10:3443

    Google Scholar 

  6. Gama S, Himashree BN, Nagashree DB, Hegde M (2019) Precisional detection of calorie information in Indian food types using image recognition to address Annorexia Nervosa. Int J Eng Sci Comput 9(3)

  7. Akhi AB, Akter F, Khatun T, Shorif Uddin M (2018) Recognition and classification of fast food images. Glob J ComputSciTechnol 18(1):6–13

    Google Scholar 

  8. Santhana Krishnan R, Golden Julie E, Harold Robinson Y, Kumar R, Hoang Son L, Anh Tuan T, Viet Long H (2020) Modified zone based intrusion detection system for security enhancement in mobile ad-hoc networks. WirelNetw 26:1275–1289

    Google Scholar 

  9. Kiourt C, Pavlidis G, Markantonatou S (2020) Deep learning approaches in food recognition, machine learning paradigms—advances in theory and applications of deep learning. Springer, Berlin

    Google Scholar 

  10. Chaudhari A, More S, Khane S, Mane H, Kamble P (2019) Object detection using convolutional neural network in the application of supplementary nutrition value of fruits. Int J Innov Technol Explor Eng 8(11)

  11. Chung DTP, Tai DV (2019) A fruits recognition system based on a modern deep learning technique. In: IOP Conference Series: Journal of Physics

  12. Subramaniyaswamy V, Manogaran G, Logesh R, Vijayakumar V, Chilamkurti N, Malathiand D, Senthilselvan N (2019) An ontology-driven personalized food recommendation in IoT-based healthcare system. J Supercomput 75(6):3184–3216

    Article  Google Scholar 

  13. Wibisono A, Wisesa HA, Rahmadhani ZP, Fahira PK, Mursanto P, Jatmiko W (2020) Traditional food knowledge of Indonesia: a new high-quality food dataset and automatic recognition system. J Big Data 6(7):1–19

    Google Scholar 

  14. Shen Z, Shehzad A, Chen S, Sun H, Liu J (2019) Machine learning based approach on food recognition and nutrition estimation. In: International Conference on Identification, Information and Knowledge in the Internet of Things

  15. Sun J, Radecka K, Zilic Z (2019) Exploring better food detection via transfer learning. In: International Conference on Machine Vision Applications (MVA)

  16. Wasif SMd, Thakery S, Nagauri A, Pereira SI (2019) Food calorie estimation using machine learning and image processing. Int J Adv Res Ideas Innov Technol 5(2)

  17. Meng L, Chen L, Yang X, Tao D, Zhang H, Miao C, Chua T-S (2019) Learning using privileged information for food recognition. In: Knowledge Processing and action analysis MM’19

  18. Lu Y (2019) Food image recognition by using convolutional neural networks. arXiv:1612.00983v2 [cs.CV]

  19. Park S-J, Palvanov A, Lee C-H, Jeong N, Cho Y-I, Lee H-J (2019) The development of food image detection and recognition model of Korean food for mobile dietary management. Nutr Res Pract 13(6):521–528

    Article  Google Scholar 

  20. Burke LE et al (2005) Self-monitoring dietary intake: current and future practices. J Renal NutrOff J Council Renal NutrNatl Kidney Found 15(3):281–290

    Article  Google Scholar 

  21. Lopez-Meyer P, Schuckers S, Makeyev O, Fontanaand JM, Sazonov E (2012) Automatic identification of the number of food items in a meal using clustering techniques based on the monitoring of swallowing and chewing. Biomed Signal Process Control 7(5):474–480

    Article  Google Scholar 

  22. Pouladzadeh P, Yassine A (2015) FooDD: food detection dataset for calorie measurement using food images. Lecture notes in Computer science

  23. Akpa EAH, Suwa H, Arakawa Y, Yasumoto K (2017) Smartphone-based food weight and calorie estimation method for effective food journaling. SICE J Control MeasSystIntegr 10:360–369

    Article  Google Scholar 

  24. Ege T, Yanai K (2017) Image-based food calorie estimation using knowledge on food categories, ingredients and cooking directions. In: Association for computing machinery

  25. Nguyen BT, Dang-Nguyen D-T, Dang TX, Phat T, Gurrin C (2018) A deep learning based food recognition system for lifelog images. In: International Conference on Pattern Recognition Applications and Methods

  26. Turmchokkasam S, Chamnongthai K (2018) The design and implementation of an ingredient based food calorie estimation system using nutrition knowledge and fusion of brightness and heat. Inform IEEE Access 6:46863–46876

    Article  Google Scholar 

  27. Mezgec S, Seljak BK, Net NA (2017) Deep learning food and drink image recognition system for dietary assessment. Nutrients 9:E657

    Article  Google Scholar 

  28. Bruno V, Resende S, Juan C (2017) A survey on automated food monitoring and dietary management systems. J Health Med Inform 8(3):272

    Google Scholar 

  29. Zhou L, Zhang C, Liu F, Qiu Z, He Y (2019) Application of deep learning in food: a review. Compreh Rev Food Sci Food Safety 18:1793–1811

    Article  Google Scholar 

  30. Jasmine MS, Emmanuel WRS (2020) Food recognition using neural network classifier and multiple hypotheses image segmentation. ImagSci J 68(2):100–113

    Google Scholar 

  31. Wibisono A, Wisesa HA, Rahmadhani ZP, Fahira PK, Mursanto P, Jatmiko W (2020) Traditional food knowledge of Indonesia: a new high-quality food dataset and automatic recognition system. J Big Data 7(69):1–19

    Google Scholar 

  32. Jiang S, Min W, Liu L, Luo Z (2020) Multi-scale multi-view deep feature aggregation for food recognition. IEEE Trans Image Process 29:265–276

    Article  MathSciNet  Google Scholar 

  33. Jha S, Prashar D, Viet Long H, Taniar D (2020) Recurrent neural network for detecting malware. Comput Secur 99:102037

    Article  Google Scholar 

  34. Pritam N, Khari M, Son LH, Kumar R, Jha S, Priyadarshini I, Basset MA (2019) Assessment of code smell for predicting class change proneness using machine learning. IEEE Access 7:37414–37425

    Article  Google Scholar 

  35. Patro SGK, Mishra BK, Panda SK, Kumar R, Long HV, Taniar D, Priyadarshini I (2020) A hybrid action-related K-nearest neighbor(HAR-KNN) approach for recommendation systems. IEEE Access 63(6):90978–90991

    Article  Google Scholar 

Download references

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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Authors and Affiliations

  1. CSE Department, Siddhartha Institute of Technology and Science, Hyderabad, India

    R. Dinesh Kumar

  2. Department of Computer Science and Engineering, Anna University Regional Campus, Tirunelveli, India

    E. Golden Julie

  3. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

    Y. Harold Robinson

  4. Department of Information Technology, National Engineering College, Kovilpatti, India

    S. Vimal

  5. School of Computer Art, College of Art and Technology, Chung-Ang University, 4726 Seodongdae-ro, Daedeok-myon, Anseong-si, Kyunggi-do, 17546, South Korea

    Sanghyun Seo

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  1. R. Dinesh Kumar
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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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  • DOI: https://doi.org/10.1007/s11227-021-03622-w

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