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UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients.
Miao, Rui; Dong, Xin; Xie, Sheng-Li; Liang, Yong; Lo, Sio-Long.
  • Miao R; Institute of Systems Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China.
  • Dong X; Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China.
  • Xie SL; Institute of Systems Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China.
  • Liang Y; Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou, 510006, China.
  • Lo SL; Institute of Systems Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China.
BMC Med Imaging ; 21(1): 174, 2021 11 22.
Article in English | MEDLINE | ID: covidwho-1528681
ABSTRACT

BACKGROUND:

With 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.

METHODS:

This 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.

RESULTS:

The 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.

CONCLUSION:

In summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Tomography, X-Ray Computed / Deep Learning / COVID-19 Type of study: Diagnostic study / Reviews Limits: Humans Language: English Journal: BMC Med Imaging Journal subject: Diagnostic Imaging Year: 2021 Document Type: Article Affiliation country: S12880-021-00704-2

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Tomography, X-Ray Computed / Deep Learning / COVID-19 Type of study: Diagnostic study / Reviews Limits: Humans Language: English Journal: BMC Med Imaging Journal subject: Diagnostic Imaging Year: 2021 Document Type: Article Affiliation country: S12880-021-00704-2