A MACHINE LEARNING-BASED APPROACH FOR RECOGNIZING AND CLASSIFYING TRADITIONAL SOUTH ASIAN FOOD ITEMS
Main Article Content
Keywords
Convolutional Neural Networks (CNN), Deep Learning, Food Image Classification, Machine Learning, SAFC_net Model, South Asian Cuisine
Abstract
Food categorization, especially of recipes from South Asians, is an innovative branch of deep learning whereby it finds uses in measurement of diet, automation of restaurants, and cultural recognition. In this work, the researchers restricted their study to determining a system to learn the classification of South Asian food using a subset of the Food-101 dataset. Classification of South Asian cuisine entails difficulties stemming from similarity in appearance of the dishes and the nature of their presentation. Hence, a pre-existing model known as Mobile net is chosen, and a few layers are added. For the classification of South Asian dishes, it was necessary to add the custom layers, namely, the Global Average Pooling and the fully connected layers. This enhancement enabled the model to better pick up the complex patterns that are stereotypically associated with South Asian dishes and enhance its categorization functionality. The specified dataset for training was preprocessed by resizing, normalizing the images, and data augmentation, which included rotation, flipping, and zooming. This helped to enhance the generalization capability of the model when it is applied to various pictures of food. During training, the Adam optimizer was employed; it obtained training accuracy up to 98%. Average of 60, mean of 08% after 50 epochs. To compare the performance of the proposed model over the different classes, accuracy, precision, recall, and F1 score were computed. Although the model has very slight confusion between visually similar foods, the overall accuracy was over 90 percent for these measures. Thus, as compared to the other models like VGG16 and ResNet50, our custom-enhanced SAFC net offered comparable accuracy but with lesser computation as required for real-time applications like the mobile food recognition APP and automated ordering systems.
References
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49. Asp, N.G., 1994. Nutritional classification and analysis of food carbohydrates. The American journal of clinical nutrition, 59(3), pp.679S-681S.
50. Goldfein, K.R. and Slavin, J.L., 2015. Why sugar is added to food: food science 101. Comprehensive Reviews in Food Science and Food Safety, 14(5), pp.644-656.
51. Majumdar, D., Koch, P.A., Lee, H., Contento, I.R., Islas-Ramos, A.D.L. and Fu, D., 2013. “Creature-101”: a serious game to promote energy balance-related behaviors among middle school adolescents. GAMES FOR HEALTH: Research, Development, and Clinical Applications, 2(5), pp.280-290.
52. Borzekowski, D.L. and Robinson, T.N., 2001. The 30-second effect: an experiment revealing the impact of television commercials on food preferences of preschoolers. Journal of the American Dietetic Association, 101(1), pp.42-46.
53. Martin, C.K., Han, H., Coulon, S.M., Allen, H.R., Champagne, C.M. and Anton, S.D., 2008. A novel method to remotely measure food intake of free-living individuals in real time: the remote food photography method. British Journal of Nutrition, 101(3), pp.446-456.
54. Medeiros, L.C., Kendall, P., Hillers, V., Chen, G. and DiMascola, S., 2001. Identification and classification of consumer food-handling behaviors for food safety education. Journal of the American Dietetic Association, 101(11), pp.1326-1339.
55. Z. Zahisham, Z. Zharfan, C.-P. Lee, and K. Lim, “Food recognition with ResNet-50,” Proc. IEEE Int. Conf. Artif. Intell. Eng. Technol. (IICAIET),2020, pp.1–5.
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2. Bossard, L., Guillaumin, M. and Van Gool, L., 2014. Food-101–mining discriminative components with random forests. In Computer vision–ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part VI 13 (pp. 446-461). Springer International Publishing.
3. Şengür, A., Akbulut, Y. and Budak, Ü., 2019, September. Food image classification with deep features. In 2019 international artificial intelligence and data processing symposium (IDAP) (pp. 1-6). Ieee.
4. Arslan, B., Memiş, S., Sönmez, E.B. and Batur, O.Z., 2021. Fine-grained food classification methods on the UEC food-100 database. IEEE Transactions on Artificial Intelligence, 3(2), pp.238-243.
5. Zahisham, Z., Lee, C.P. and Lim, K.M., 2020, September. Food recognition with resnet-50. In 2020 IEEE 2nd international conference on artificial intelligence in engineering and technology (IICAIET) (pp. 1-5). IEEE.
6. Ittisoponpisan, S., Kaipan, C., Ruang-on, S., Thaiphan, R. and Songsri-in, K., 2022. Pushing the accuracy of thai food image classification with transfer learning. Engineering Journal, 26(10), pp.57-71.
7. Mahaveerakannan, R. and Devi, K.K., 2024, April. Food Classification by extracting the important features using VGGNet based Models in Precision Agriculture. In 2024 2nd International Conference on Networking and Communications (ICNWC) (pp. 1-7). IEEE.
8. Şengür, A., Akbulut, Y. and Budak, Ü., 2019, September. Food image classification with deep features. In 2019 international artificial intelligence and data processing symposium (IDAP) (pp. 1-6). Ieee.
9. Xu, B., He, X. and Qu, Z., 2021. Asian food image classification based on deep learning. Journal of Computer and Communications, 9(03), p.10.
10. Singla, A., Yuan, L. and Ebrahimi, T., 2016, October. Food/non-food image classification and food categorization using pre-trained googlenet model. In Proceedings of the 2nd international workshop on multimedia assisted dietary management (pp. 3-11).
11. Hnoohom, N. and Yuenyong, S., 2018, February. Thai fast food image classification using deep learning. In 2018 International ECTI northern section conference on electrical, electronics, computer and telecommunications engineering (ECTI-NCON) (pp. 116-119). IEEE.
12. Alahmari, S.S., Gardner, M.R. and Salem, T., 2024. Attention guided approach for food type and state recognition. Food and Bioproducts Processing.
13. Suddul, G. and Seguin, J.F.L., 2023. A comparative study of deep learning methods for food classification with images. Food and Humanity, 1, pp.800-808.
14. Srigurulekha, K. and Ramachandran, V., 2020, January. Food image recognition using CNN. In 2020 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-7). IEEE.
15. Siemon, M.S., Shihavuddin, A.S.M. and Ravn-Haren, G., 2021. Sequential transfer learning based on hierarchical clustering for improved performance in deep learning based food segmentation. Scientific Reports, 11(1), p.813.
16. Hnoohom, N. and Yuenyong, S., 2018, February. Thai fast food image classification using deep learning. In 2018 International ECTI northern section conference on electrical, electronics, computer and telecommunications engineering (ECTI-NCON) (pp. 116-119). IEEE.
17. Rajayogi, J.R., Manjunath, G. and Shobha, G., 2019, December. Indian food image classification with transfer learning. In 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) (pp. 1-4). IEEE.
18. Yadav, S. and Chand, S., 2021, March. Automated food image classification using deep learning approach. In 2021 7th international conference on advanced computing and communication systems (ICACCS) (Vol. 1, pp. 542-545). IEEE.
19. Iftikhar, A., Bilal, A., Rakha, B. A., & Akhter, S. (2025). Evaluating the Cryoprotective Effects of Butylated Hydroxytoluene on Semen Quality Parameters of Phasianus colchicus. Journal of Agriculture and Biology, 3(1), 1.
20. Ameer, S., Mehmood, M. A., Khan, W. A., Arsalan, M., Hassan, N. U., Noor, A., Bukhari, S. S. A. S., Tahir, A., & Manazir, A. (2024a, September 24). Detection of lung cancer through computed tomographic images using deep learning models. Journal of Population Therapeutics and Clinical Pharmacology, 31(9),
21. Chun, M., Jeong, H., Lee, H., Yoo, T. and Jung, H., 2022. Development of Korean food image classification model using public food image dataset and deep learning methods. IEEE Access, 10, pp.128732-128741.
22. Bilal, A., Bibi, R., Umar, M., Sajjad, A., Kharal, S., Noor, E., ... & Munir, A. (2025). The relationship between obesity and breast cancer among women of Punjab, Pakistan. The Research of Medical Science Review, 3(2), 668-84.
23. Shahin, F., Ishfaq, A., Asif, I., Bilal, A., Masih, S., Ashraf, T., ... & Ishfaq, R. (2024). CRISPR-Cas Innovative Strategies for Combating Viral Infections and Enhancing Diagnostic Technologies: CRISPR-Cas in Viral Diagnostics and Therapeutics. Journal of Health and Rehabilitation Research, 4(3), 1-4.
24. Ahmad, R. Z., Khan, M. S., Bilal, A., Ali, U., & Sattar, R. Z. (2023). Effect of locus of control and depression among young adults in Multan (Pakistan). Journal of Asian Development Studies, 12(4), 684-692.
25. Bilal, A. (2021a). Rabies is a zoonotic disease: a literature review. Occup. Med. Health Aff, 9(2).
26. Bilal, A. (2021b). Clinical diagnosis and treatment of absence seizures: Case study. MAR Ophthalmology, 2(1)
27. Sattar, R. Z., Bilal, A., Bashir, S., Iftikhar, A., & Yaqoob, I. (2024). Embryotoxicity of fluconazole on developing chick embryos. The Journal of Basic and Applied Zoology, 85(1), 8.
28. Chun, M., Jeong, H., Lee, H., Yoo, T. and Jung, H., 2022. Development of Korean food image classification model using public food image dataset and deep learning methods. IEEE Access, 10, pp.128732-128741.
29. Gao, X., Xiao, Z. and Deng, Z., 2024. High accuracy food image classification via vision transformer with data augmentation and feature augmentation. Journal of Food Engineering, 365, p.111833.
30. Pan, X., He, J. and Zhu, F., 2024. FMiFood: Multi-modal Contrastive Learning for Food Image Classification. arXiv preprint arXiv:2408.03922.
31. Raghavan, S., He, J. and Zhu, F., 2024. Online class-incremental learning for real-world food image classification. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 8195-8204).
32. Oduru, T., Jordan, A. and Park, A., 2022. Healthy vs. unhealthy food images: Image classification of twitter images. International Journal of Environmental Research and Public Health, 19(2), p.923.
33. R. Patel and N. Sharma, "Kaathi rolls: A fusion of Indian and international flavors," *International Journal of Street Food Studies*, vol. 7, no. 2, pp. 221-229, 2023.
34. Shah, B. and Bhavsar, H., 2024. Depth-restricted convolutional neural network—A model for Gujarati food image classification. The Visual Computer, 40(3), pp.1931-1946.
35. Farinella, G.M., Allegra, D., Moltisanti, M., Stanco, F. and Battiato, S., 2016. Retrieval and classification of food images. Computers in biology and medicine, 77, pp.23-39.
36. Medus, L.D., Saban, M., Francés-Víllora, J.V., Bataller-Mompeán, M. and Rosado-Muñoz, A., 2021. Hyperspectral image classification using CNN: Application to industrial food packaging. Food Control, 125, p.107962.
37. Ciocca, G., Napoletano, P. and Schettini, R., 2018. CNN-based features for retrieval and classification of food images. Computer Vision and Image Understanding, 176, pp.70-77.
38. Fahira, P.K., Rahmadhani, Z.P., Mursanto, P., Wibisono, A. and Wisesa, H.A., 2020, November. Classical machine learning classification for javanese traditional food image. In 2020 4th International Conference on Informatics and Computational Sciences (ICICoS) (pp. 1-5). IEEE.
39. Lo, F.P.W., Sun, Y., Qiu, J. and Lo, B., 2020. Image-based food classification and volume estimation for dietary assessment: A review. IEEE journal of biomedical and health informatics, 24(7), pp.1926-1939.
40. Kazi, A. and Panda, S.P., 2022. Determining the freshness of fruits in the food industry by image classification using transfer learning. Multimedia Tools and Applications, 81(6), pp.7611-7624.
41. Ireland, J.D. and Møller, A., 2000. Review of international food classification and description. Journal of food composition and analysis, 13(4), pp.529-538.
42. Petrus, R.R., do Amaral Sobral, P.J., Tadini, C.C. and Gonçalves, C.B., 2021. The NOVA classification system: A critical perspective in food science. Trends in Food Science & Technology, 116, pp.603-608.
43. Zheng, W., Fu, X. and Ying, Y., 2014. Spectroscopy-based food classification with extreme learning machine. Chemometrics and Intelligent Laboratory Systems, 139, pp.42-47.
44. Cianferoni, A. and Spergel, J.M., 2009. Food allergy: review, classification and diagnosis. Allergology International, 58(4), pp.457-466.
45. Ameer, S., Khan, M. T. A., Fahad, S., Mumtaz, A., Fatima, H. S., Rahman, T. U., Bukhari, S. S. A., Sajjad, A., Hassan, S. A., & Aziz, S. (2024b, September 25). A machine learning-based approach for the detection and segmentation of retinal vessels in ophthalmic disease analysis. Journal of Population Therapeutics and Clinical Pharmacology, 31(9), 13. RESEARCHTRENTZ Acad Publ Education Services.
46. Cianferoni, A. and Spergel, J.M., 2009. Food allergy: review, classification and diagnosis. Allergology International, 58(4), pp.457-466.
47. Päßler, S., Wolff, M. and Fischer, W.J., 2012. Food intake monitoring: an acoustical approach to automated food intake activity detection and classification of consumed food. Physiological measurement, 33(6), p.1073.
48. Marrón-Ponce, J.A., Sánchez-Pimienta, T.G., da Costa Louzada, M.L. and Batis, C., 2018. Energy contribution of NOVA food groups and sociodemographic determinants of ultra-processed food consumption in the Mexican population. Public health nutrition, 21(1), pp.87-93..
49. Asp, N.G., 1994. Nutritional classification and analysis of food carbohydrates. The American journal of clinical nutrition, 59(3), pp.679S-681S.
50. Goldfein, K.R. and Slavin, J.L., 2015. Why sugar is added to food: food science 101. Comprehensive Reviews in Food Science and Food Safety, 14(5), pp.644-656.
51. Majumdar, D., Koch, P.A., Lee, H., Contento, I.R., Islas-Ramos, A.D.L. and Fu, D., 2013. “Creature-101”: a serious game to promote energy balance-related behaviors among middle school adolescents. GAMES FOR HEALTH: Research, Development, and Clinical Applications, 2(5), pp.280-290.
52. Borzekowski, D.L. and Robinson, T.N., 2001. The 30-second effect: an experiment revealing the impact of television commercials on food preferences of preschoolers. Journal of the American Dietetic Association, 101(1), pp.42-46.
53. Martin, C.K., Han, H., Coulon, S.M., Allen, H.R., Champagne, C.M. and Anton, S.D., 2008. A novel method to remotely measure food intake of free-living individuals in real time: the remote food photography method. British Journal of Nutrition, 101(3), pp.446-456.
54. Medeiros, L.C., Kendall, P., Hillers, V., Chen, G. and DiMascola, S., 2001. Identification and classification of consumer food-handling behaviors for food safety education. Journal of the American Dietetic Association, 101(11), pp.1326-1339.
55. Z. Zahisham, Z. Zharfan, C.-P. Lee, and K. Lim, “Food recognition with ResNet-50,” Proc. IEEE Int. Conf. Artif. Intell. Eng. Technol. (IICAIET),2020, pp.1–5.
56. M. S. Zaoad, M. M. R. Mannan, A. B. Mandol, M. Rahman, M. A. Islam, and M. M. Rahman, “An attention-based hybrid deep learning approach for Bengali video captioning,” J. King Saud Univ. - Comput. Inf. Sci., vol. 35, no. 1, pp. 257–269, 2023, doi: 10.1016/j.jksuci.2022.11.015.
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Aug 20, 2024
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How to Cite
Muhammad Hamza Gull, Muhammad Suleman Shahzad, Samreen Razzaq, Shahid Ameer, Abdullah Bin Masood, Muhammad Mohsin, Emal Khaliq Dad, & Muzzamil Rani. (2024). A MACHINE LEARNING-BASED APPROACH FOR RECOGNIZING AND CLASSIFYING TRADITIONAL SOUTH ASIAN FOOD ITEMS. Journal of Population Therapeutics and Clinical Pharmacology, 31(8), 3845-3865. https://doi.org/10.53555/vx9b3x07
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Author Biographies
Muhammad Hamza Gull
Department of Software Engineering, University of Sargodha, Pakistan
Muhammad Suleman Shahzad
Department of Computer Science and Information Technology, Superior University Lahore, Pakistan
Samreen Razzaq
Department of Computer Science, University of Sargodha, Pakistan
Shahid Ameer
Department of Computer Science and Information Technology, Superior University Lahore, Pakistan
Abdullah Bin Masood
Department of Allied Health Sciences, Superior University Lahore, Pakistan
Muhammad Mohsin
Department of Biological Sciences, Superior University Lahore, Pakistan
Emal Khaliq Dad
Department of Biological Sciences, Superior University Lahore, Pakistan
Muzzamil Rani
Department of Computer Science, University of Sargodha, Pakistan
How to Cite
Muhammad Hamza Gull, Muhammad Suleman Shahzad, Samreen Razzaq, Shahid Ameer, Abdullah Bin Masood, Muhammad Mohsin, Emal Khaliq Dad, & Muzzamil Rani. (2024). A MACHINE LEARNING-BASED APPROACH FOR RECOGNIZING AND CLASSIFYING TRADITIONAL SOUTH ASIAN FOOD ITEMS. Journal of Population Therapeutics and Clinical Pharmacology, 31(8), 3845-3865. https://doi.org/10.53555/vx9b3x07