CaloriScan – Simple and Clear: Calorie Scanning From Images
Year: 2025
Type: article
Abstract: This review paper examines the development and application of AI-based systems for automated food calorie and nutrition estimation from images. With rising global health con- cerns related to diet-related diseases, accurate dietary tracking has become essential for health management. Traditional manual food logging methods are time-consuming and prone to errors, creating a need for automated solutions. This paper synthesizes current research on computer vision and deep learning tech- niques applied to food recognition, portion size estimation, and nutritional analysis. The review discusses various methodological approaches, technological architectures, performance metrics, practical applications, and existing challenges in this rapidly evolving field. The paper concludes by examining future direc- tions and the potential impact of these systems on public health and personalized nutrition
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4416156200