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Dietary Assessment and Nutritional Analysis Using Deep Learning

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 844)


Nutrition management is an important feature in day-to-day life from prevention of obesity and chronic diseases related to food intake. To facilitate proper and adequate nutrition intake, suitable dietary assessment is essential. This dietary assessment method based on food recognition has the ability to detect the volume, based on the area of the image taken through computer vision. There has been many modern techniques developed recently on image-based dietary assessment. These techniques have shown trustworthy results on overcoming issues on epidemiology on dietary studies and multiple challenges. This model provides a detailed outline of how features are extracted from the image using computing algorithms, methodology used, and mathematical methods for image recognition. This model provides volume and mass estimation of the given food based on the area occupied by the food. From this, the detailed nutritional analysis was calculated for dietary assessment. The overall accuracy of 94.68% was achieved for classification of food which outperforms the existing methods.


  • Volume estimation
  • Food detection
  • Dietary assessment
  • Nutritional analysis

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  • DOI: 10.1007/978-981-16-8862-1_2
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Madhumitha, S., Magimaa, M., Maniratnam, M., Neelima, N. (2022). Dietary Assessment and Nutritional Analysis Using Deep Learning. In: Bindhu, V., Tavares, J.M.R.S., Du, KL. (eds) Proceedings of Third International Conference on Communication, Computing and Electronics Systems . Lecture Notes in Electrical Engineering, vol 844. Springer, Singapore.

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