Image-based food groups and portion prediction by using deep learning

Abstract

Chronic diseases such as obesity and hypertension due to malnutrition can be prevented by following the appropriate diet, correct diet intake with correct measuring portion size, and developing healthy eating habits. Having a system that can automatically measure food consumption is important to determine whether individual nutritional needs are being met in order to accurately diagnose and solve nutritional problems, act quickly, and minimize the risk of malnutrition due to the cross-cultural diversity of foods. In this study, a deep learning system has been developed and implemented for automatically grouping and classifying foods. Dishes from Turkish cuisine were chosen as a sample for application and testing. The deep learning method used in this system is convolutional neural network (CNN) models based on image recognition. This study developed and implemented a deep learning system using CNNs to classify food groups and estimate portion sizes of Turkish cuisine dishes, achieving accuracy rates of up to 80% for food group classification and 80.47% for portion estimation with the inclusion of data augmentation.