Abstract: Objective To built a diet evaluation system based on artificial intelligence to evaluate the daily diet of patients with chronic diseases.Methods Deep learning technology and traditional image processing method were used to realize intelligent segmentation, recognition and nutrient estimation of food image, so that patients with chronic diseases could obtain the nutrient information of food directly with only the food images taken by smart phones. The system also supported the fine-grained recognition of 172 Chinese food recipes and 353 food ingredients which had been verified in the food dataset Vireo Food-172. Results The predictive accuracy of the recipes based on the convolutional neural network model was 89.72%, the micro-averaging(Micro-F1) and macro-averaging(Macro-F1) of ingredients improved to 79.06% and 64.28% respectively. The state-of-the-art performance of ingredients recognition was achieved on food dataset Vireo Food-172; The food nutrients were estimated based on the results of recipe classification and ingredients recognition, and the error between the estimated values and the reference values was within a reasonable range. Conclusion The system could realize intelligent dietary assessment for patients with chronic diseases, which could facilitate patients to self-supervise their daily diet and assist nutritionists to complete the daily diet record and assessment. It had practical value and research significance.

Key words: chronic diseases, food image, deep learning, super-pixel segmentation, image classification, nutrition estimation