ArticleScientific reports2025
Improved food image recognition by leveraging deep learning and data-driven methods with an application to Central Asian Food Scene.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Self-Management of Gestational Diabetes Mellitus: A Systematic Review.Journal of medical systems · 2026Pooled it
- A Review of Machine Learning and AI Applications in Enhancing HACCP Systems for Ice Cream Manufacturing.Foods (Basel, Switzerland) · 2026Review
- Development and Validation of a Photographic Food Atlas for Portion Size Estimation and Educational Use in Türkiye.Journal of human nutrition and dietetics : the official journal of the British Dietetic Association · 2026Article
- Chinese Food Images for Full-cycle Nutrition Analysis Towards Diabetes Management.Scientific data · 2026Article
- Digital Mapping of Central Asian Foods: Towards a Standardized Visual Atlas for Nutritional Research.Nutrients · 2025Article
- Dietary Intake Mendelian Randomization: Assessment and Development of Methods for Instrument Selection and Robust Inference.medRxiv : the preprint server for health sciences · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
The burden of diet-related diseases is high in Central Asia. In recent years, the field of food computing has gained prominence due to advancements in computer vision (CV) and the increasing use of smartphones and social media. These technologies provide promising potential in many applications by facilitating real-time information retrieval from food images for efficient digital food journaling, smart restaurants, and supermarkets etc. Yet, to develop a robust CV model for food information retrieval, a large-scale high quality dataset is required. Several food dataset have been developed covering Western, Mediterranean, Chinese etc. cuisines. These dataset solve the simpler problem of food classification with single food item per image, which is not practical for real-life scenarios, where meals typically consist of multiple food items. To address this gap, we developed a large-scale high-quality Central Asian Food Scenes Dataset for food localization and detection. The dataset contains 21,306 images across 239 food categories, 69,856 instances. ed images. To evaluate the dataset, we performed the parametric experiments with the object detection models, with the best results achieved using YOLOv8xl (mAP50 score of 0.677).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.