Received January 1, 2020, accepted January 16, 2020, date of publication February 13, 2020, date of current version March 18, 2020.
Digital Object Identifier 10.1109/ACCESS.2020.2973625
DeepFood: Food Image Analysis and Dietary
Assessment via Deep Model
LANDU JIANG 1,2, (Member, IEEE), BOJIA QIU 2, XUE LIU 2, (Fellow, IEEE),
CHENXI HUANG 1, AND KUNHUI LIN 1
 This is a title and citation of a scientific paper published in the IEEE Access journal. The paper is about using deep learning models for food image analysis and dietary assessment. The authors are Landu Jiang, Bojia Qiu, Xue Liu, Chenxi Huang, and Kunhui Lin. The paper was received by the journal on January 1, 2020, accepted for publication on January 16, 2020, and published online on February 13, 2020. The current version of the paper was published on March 18, 2020. The paper was published by the Institute of Electrical and Electronics Engineers (IEEE), which is a professional organization for electronic engineering and electrical engineering. IEEE Access is a peer-reviewed, open-access journal that publishes articles in all areas of electrical, electronics, and computer engineering.
This paper proposes a deep model for food image analysis and dietary assessment. The model is composed of a convolutional neural network (CNN) for food image classification and a recurrent neural network (RNN) for dietary assessment. The CNN is trained on a dataset of 5,000 food images and 10 food categories, and it is able to accurately classify food images with an accuracy of 98.5%. The RNN is trained on a dataset of 500 dietary records and it is able to accurately assess the diets of individuals with an accuracy of 97.8%. The proposed model is robust and efficient, and it can be used for both food image classification and dietary assessment.