Comparison of convolutional neural network models for food image classification

According to some estimates of World Health Organization (WHO), in 2014, more than 1.9 billion adults aged 18 years and older were overweight. Overall, about 13% of the world's adult population (11% of men and 15% of women) were obese. 39% of adults aged 18 years and over (38% of men and 40% of...

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Published in2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) pp. 349 - 353
Main Authors Ozsert Yigit, Gozde, Ozyildirim, Buse Melis
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2017
Subjects
Convolution
Convolutional Neural Network
Deep learning
Diabetes
Food Classification
Graphics processing units
Kernel
Neural networks
Nutrition Categorization
Obesity
Training
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Summary:According to some estimates of World Health Organization (WHO), in 2014, more than 1.9 billion adults aged 18 years and older were overweight. Overall, about 13% of the world's adult population (11% of men and 15% of women) were obese. 39% of adults aged 18 years and over (38% of men and 40% of women) were overweight. The worldwide prevalence of obesity more than doubled between 1980 and 2014. The purpose of this study is to design a convolutional neural network model and provide a food dataset collection to distinguish the nutrition groups which people take in daily life. For this aim, both two pretrained models Alexnet and Caffenet were finetuned and a similar structure was trained with dataset. Food images were generated from Food-11, FooDD, Food100 datasets and web archives. According to the test results, finetuned models provided better results than trained structure as expected. However, trained model can be improved by using more training examples and can be used as specific structure for classification of nutrition groups.
DOI:10.1109/INISTA.2017.8001184