ArticleJournal of imaging2024
Fine-Grained Food Image Recognition: A Study on Optimising Convolutional Neural Networks for Improved Performance.
Article in Journal of imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Seeing What's on the Plate: Composition-Aware Fine-Grained Food Recognition for Dietary Analysis.Foods (Basel, Switzerland) · 2026Article
- Enhancing dietary management and nutritional analysis through deep learning: a multi-model approach for food classification and volume estimation.Scientific reports · 2026Article
- Visual Food Ingredient Prediction Using Deep Learning with Direct F-Score Optimization.Foods (Basel, Switzerland) · 2025Article
- An Explainable CNN and Vision Transformer-Based Approach for Real-Time Food Recognition.Nutrients · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Addressing the pressing issue of food waste is vital for environmental sustainability and resource conservation. While computer vision has been widely used in food waste reduction research, existing food image datasets are typically aggregated into broad categories (e.g., fruits, meat, dairy, etc.) rather than the fine-grained singular food items required for this research. The aim of this study is to develop a model capable of identifying individual food items to be integrated into a mobile application that allows users to photograph their food items, identify them, and offer suggestions for recipes. This research bridges the gap in available datasets and contributes to a more fine-grained approach to utilising existing technology for food waste reduction, emphasising both environmental and research significance. This study evaluates various (
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Registered trials
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.