A Framework to Estimate the Nutritional Value of Food in Real Time Using Deep Learning Techniques
There has been a rapid increase in dietary ailments during the last few decades, caused by unhealthy food routine. Mobile-based dietary assessment systems that can record real-time images of the meal and analyze it for nutritional content can be very handy and improve the dietary habits and, therefo...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8590712/ |
| _version_ | 1818558643378847744 |
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| author | Raza Yunus Omar Arif Hammad Afzal Muhammad Faisal Amjad Haider Abbas Hira Noor Bokhari Syeda Tazeen Haider Nauman Zafar Raheel Nawaz |
| author_facet | Raza Yunus Omar Arif Hammad Afzal Muhammad Faisal Amjad Haider Abbas Hira Noor Bokhari Syeda Tazeen Haider Nauman Zafar Raheel Nawaz |
| author_sort | Raza Yunus |
| collection | DOAJ |
| description | There has been a rapid increase in dietary ailments during the last few decades, caused by unhealthy food routine. Mobile-based dietary assessment systems that can record real-time images of the meal and analyze it for nutritional content can be very handy and improve the dietary habits and, therefore, result in a healthy life. This paper proposes a novel system to automatically estimate food attributes such as ingredients and nutritional value by classifying the input image of food. Our method employs different deep learning models for accurate food identification. In addition to image analysis, attributes and ingredients are estimated by extracting semantically related words from a huge corpus of text, collected over the Internet. We performed experiments with a dataset comprising 100 classes, averaging 1000 images for each class to acquire top 1 classification rate of up to 85%. An extension of a benchmark dataset Food-101 is also created to include sub-continental foods. Results show that our proposed system is equally efficient on the basic Food-101 dataset and its extension for sub-continental foods. The proposed system is implemented as a mobile app that has its application in the healthcare sector. |
| first_indexed | 2024-12-14T00:14:58Z |
| format | Article |
| id | doaj.art-01be7410fcaa4b2095aa86d75ace861c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T00:14:58Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-01be7410fcaa4b2095aa86d75ace861c2022-12-21T23:25:35ZengIEEEIEEE Access2169-35362019-01-0172643265210.1109/ACCESS.2018.28791178590712A Framework to Estimate the Nutritional Value of Food in Real Time Using Deep Learning TechniquesRaza Yunus0https://orcid.org/0000-0002-3979-9038Omar Arif1Hammad Afzal2https://orcid.org/0000-0001-9583-5585Muhammad Faisal Amjad3Haider Abbas4Hira Noor Bokhari5Syeda Tazeen Haider6Nauman Zafar7Raheel Nawaz8School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, PakistanSchool of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, PakistanCollege of Signals, National University of Sciences and Technology, Islamabad, PakistanCollege of Signals, National University of Sciences and Technology, Islamabad, PakistanCollege of Signals, National University of Sciences and Technology, Islamabad, PakistanSchool of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, PakistanSchool of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, PakistanSchool of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, PakistanDepartment of Operations, Technology, Events and Hospitality Management, Manchester Metropolitan University, Manchester, U.K.There has been a rapid increase in dietary ailments during the last few decades, caused by unhealthy food routine. Mobile-based dietary assessment systems that can record real-time images of the meal and analyze it for nutritional content can be very handy and improve the dietary habits and, therefore, result in a healthy life. This paper proposes a novel system to automatically estimate food attributes such as ingredients and nutritional value by classifying the input image of food. Our method employs different deep learning models for accurate food identification. In addition to image analysis, attributes and ingredients are estimated by extracting semantically related words from a huge corpus of text, collected over the Internet. We performed experiments with a dataset comprising 100 classes, averaging 1000 images for each class to acquire top 1 classification rate of up to 85%. An extension of a benchmark dataset Food-101 is also created to include sub-continental foods. Results show that our proposed system is equally efficient on the basic Food-101 dataset and its extension for sub-continental foods. The proposed system is implemented as a mobile app that has its application in the healthcare sector.https://ieeexplore.ieee.org/document/8590712/Food recognitionconvolutional neural networksvector embeddingsattribute estimation |
| spellingShingle | Raza Yunus Omar Arif Hammad Afzal Muhammad Faisal Amjad Haider Abbas Hira Noor Bokhari Syeda Tazeen Haider Nauman Zafar Raheel Nawaz A Framework to Estimate the Nutritional Value of Food in Real Time Using Deep Learning Techniques IEEE Access Food recognition convolutional neural networks vector embeddings attribute estimation |
| title | A Framework to Estimate the Nutritional Value of Food in Real Time Using Deep Learning Techniques |
| title_full | A Framework to Estimate the Nutritional Value of Food in Real Time Using Deep Learning Techniques |
| title_fullStr | A Framework to Estimate the Nutritional Value of Food in Real Time Using Deep Learning Techniques |
| title_full_unstemmed | A Framework to Estimate the Nutritional Value of Food in Real Time Using Deep Learning Techniques |
| title_short | A Framework to Estimate the Nutritional Value of Food in Real Time Using Deep Learning Techniques |
| title_sort | framework to estimate the nutritional value of food in real time using deep learning techniques |
| topic | Food recognition convolutional neural networks vector embeddings attribute estimation |
| url | https://ieeexplore.ieee.org/document/8590712/ |
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