ArticleBMC medical imaging2021
UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients.
Article in BMC medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Classification and detection of dental images using meta-learning.World journal of clinical cases · 2024Article
- COV-MobNets: a mobile networks ensemble model for diagnosis of COVID-19 based on chest X-ray images.BMC medical imaging · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- How much BiGAN and CycleGAN-learned hidden features are effective for COVID-19 detection from CT images? A comparative study.The Journal of supercomputing · 2023Article
- Identification of Dementia & Mild Cognitive Impairment in Chinese Elderly Using Machine Learning.American journal of Alzheimer's disease and other dementiasArticle
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Authors and funding
5 authors.
Funding
Abstract
backgroundWith the rapid spread of COVID-19 worldwide, quick screening for possible COVID-19 patients has become the focus of international researchers. Recently, many deep learning-based Computed Tomography (CT) image/X-ray image fast screening models for potential COVID-19 patients have been proposed. However, the existing models still have two main problems. First, most of the existing supervised models are based on pre-trained model parameters. The pre-training model needs to be constructed on a dataset with features similar to those in COVID-19 X-ray images, which limits the construction and use of the model. Second, the number of categories based on the X-ray dataset of COVID-19 and other pneumonia patients is usually imbalanced. In addition, the quality is difficult to distinguish, leading to non-ideal results with the existing model in the multi-class classification COVID-19 recognition task. Moreover, no researchers have proposed a COVID-19 X-ray image learning model based on unsupervised meta-learning.
methodsThis paper first constructed an unsupervised meta-learning model for fast screening of COVID-19 patients (UMLF-COVID). This model does not require a pre-trained model, which solves the limitation problem of model construction, and the proposed unsupervised meta-learning framework solves the problem of sample imbalance and sample quality.
resultsThe UMLF-COVID model is tested on two real datasets, each of which builds a three-category and four-category model. And the experimental results show that the accuracy of the UMLF-COVID model is 3-10% higher than that of the existing models.
conclusionIn summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.
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Registered trials
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