Work

Recommendation Method Based on Glycemic Index for Intake Order of Foods Detected by Deep Learning

Year: 2025

Type: article

Abstract: In this paper, we propose a recommendation method for food intake order based on the glycemic index (GI) using deep learning to reduce rapid blood sugar spikes during meals. The foods in a captured image are classified through a food detection network. The GIs for the detected foods are found by matching their names or categories with the information stored in the database. If the detected food name or category is not found in the database, the food information is found from a public API. The food is classified into one of the food categories based on nutrients, and the median GI of the corresponding category is assigned to the food. The food intake order is recommended from the lowest to the highest GI. We implemented a web service that visualizes the food analysis results and the recommended food intake order. In experimental results, the average inference time and accuracy were 57.1 ms and 98.99% for Mask R-CNN, respectively, and 24.4 ms and 91.72% for YOLOv11, respectively.

Source: Electronics

DOI: 10.3390/electronics14030457

Authors: Jae-young Lee, Soon-kak Kwon

Institution: Dong-Eui University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $2,896