An Automatic Nutrition Estimation Framework Based on Food Images from Diabetic Patients

With the burgeoning prevalence of diabetes and the urgent imperative for effective dietary management, harnessing the power of image analysis and computer vision technology presents a promising solution to the automatic nutrition estimation challenge. However, constructing a comprehensive frame-work for this endeavor presents its own set of challenges, encompassing multiple intricate procedures, including data collection, image segmentation, food recognition, and volume estimation. In this paper, we introduce a sophisticated and automated framework for Chinese food nutrition estimation, leveraging food images sourced from diabetic patients. In addition to developing models for the various procedures, the framework necessitates label images for model training. These label images encompass ground truth masks delineating precise object boundaries for image segmentation, food type labels for food recognition, and comprehensive nutrition tables for each food item. Through rigorous experimentation and validation, our framework has demonstrated its efficacy, offering a convenient and practical tool for managing dietary requirements in diabetes care.