ArticleScientific reports2021
Sequential transfer learning based on hierarchical clustering for improved performance in deep learning based food segmentation.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review.Nutrients · 2024Pooled it
- Advances and opportunities in measuring dietary intake: from omics to AI.Nature metabolism · 2026Review
- Comparison of hierarchical clustering and neural network clustering: an analysis on precision dominance.Scientific reports · 2023Article
- A proficiency assessment of integrating machine learning (ML) schemes on Lahore water ensemble.Scientific reports · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately segmenting foods from optical images is a challenging task, yet becoming possible with the help of recent advances in Deep Learning based solutions. Automated identification of food items opens up possibilities of useful applications like nutrition intake monitoring. Given large variations in food choices, Deep Learning based solutions still struggle to generate human level accuracy. In this work, we propose a novel Sequential Transfer Learning method using Hierarchical Clustering. This novel approach simulates a step by step problem solving framework based on clustering of similar types of foods. The proposed approach provides up to 6% gain in accuracy compared to traditional network training and generated a robust model performing better in challenging unseen cases. This approach is also tested for segmenting foods in Danish school children meals for dietary intake monitoring as an application.
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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.