Deep Learning for Food Image Recognition and Nutrition Analysis Towards Chronic Diseases Monitoring: A Systematic Review

Deep Learning for Food Image Recognition and Nutrition Analysis Towards Chronic Diseases Monitoring: A Systematic Review

연구 분야: Artificial Intelligence

학회: SN Computer Science

The management of daily food intake aids to preserve a healthy body, minimize the risk of many diseases, and monitor chronic diseases, such as diabetes and heart problems. To ensure a healthy food intake, artificial intelligence has been widely used for food image recognition and nutrition analysis. Several approaches have been generated using a powerful type of machine learning: deep learning. In this paper, a systematic review is presented for the application of deep learning in food image recognition and nutrition analysis. A methodology of systematic research has been adopted resulting in three main fields: food image classification, food image segmentation and volume estimation of food items providing nutritional information.“57” original articles were selected and synthesized based on the use case of the approach, the employed model, the used data set, the experiment process and finally the main results. In addition, articles of public and private food data sets are presented. It is noted that among the literature review, several deep learning-based studies have shown great results and outperform the conventional methods. However, certain challenges and limitations are presented. Hence, some research directions are proposed to apply in the future to improve the food recognition systems for dietary assessment.