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Original Article
A Deep Learning–Based Framework for Automated Food Detection, Nutrient Estimation, and Personalized Diet Monitoring
Ojashwini RN1
Yashas A R2
Spoorthi K P3
Vinitha N S4
Sreekanta T S5
1 Assistant Professor, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India. 2 3 4 5 Department of Computer Science and Engineering, Rajarajeswari College of Engineering ,Bengaluru, Karnataka, India.
Published Online: September-December 2025
Pages: 369-376
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403056
References
1. J. He, Z. Shao, J. Wright, D. Kerr, H. Boushey, and F. Zhu, “Performing Multiple Tasks in Image-Based Dietary Assessment for Food Recognition and Portion Size Estimation,” IEEE Transactions on Multimedia, vol. XX, no. XX, pp. 1–14, Year.
2. R. Mao, J. He, Z. Shao, S. K. Yarlagadda, and F. Zhu, “Visually Aware Hierarchical Food Recognition,” in Proc. IEEE International Conference on Image Processing (ICIP), Year, pp. XX–XX.
3. Y. Shen, Z. Wu, and H. Hu, “Distilling Knowledge by Mimicking Features,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. XX, no. XX, pp. 1– 12, Year.
4. S. Park, Y. Hong, B. Heo, S. Yun, and J. Y. Choi, “The Majority Can Help the Minority: Setting-Rich Minority Oversampling for Long-Tailed Classification,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2022, pp. 6887–6896.
2. R. Mao, J. He, Z. Shao, S. K. Yarlagadda, and F. Zhu, “Visually Aware Hierarchical Food Recognition,” in Proc. IEEE International Conference on Image Processing (ICIP), Year, pp. XX–XX.
3. Y. Shen, Z. Wu, and H. Hu, “Distilling Knowledge by Mimicking Features,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. XX, no. XX, pp. 1– 12, Year.
4. S. Park, Y. Hong, B. Heo, S. Yun, and J. Y. Choi, “The Majority Can Help the Minority: Setting-Rich Minority Oversampling for Long-Tailed Classification,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2022, pp. 6887–6896.
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