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A pyramid GNN model for CXR-based COVID-19 classification

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Abstract

The urgent need for efficient COVID-19 diagnosis has spurred advancements in chest X-ray (CXR) radiography, particularly with the aid of deep learning technologies like convolutional neural networks (CNNs) and graph neural networks (GNNs). Yet, the scarcity of labeled CXR images due to privacy constraints and the complexity of COVID-19 phenotypes often hamper model performance. In this study, we present an innovative pyramid GNN model that effectively tackles these challenges. By segmenting a CXR image into patches, our model leverages a CNN to capture shallow features, then employs a pyramid graph structure within GNN layers to gain the inter-relationship of infected region in distant patches and to amalgamate high-level features. These are subsequently processed by a multi-layer perceptron classifier for final diagnosis. Our approach offers multiple benefits, including noise elimination without the need for pre-treatment, efficient examination of remote infection regions, and the ability to accommodate the intricate structure of the lungs. Evaluations conducted on three distinct public CXR image datasets suggest that our pyramid GNN model offers a promising pathway for enhancing the accuracy and efficiency of COVID-19 diagnosis.

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Data availability

The datasets used in this research work are available at various repositories such as the Kaggle COVID-19 radiography dataset, Kaggle X-ray COVID-19 dataset and Kaggle CoronaHack chest X-ray dataset.

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Funding

This work was supported by the Natural Science Foundation of Anhui Province [Grant number 2108085MF205], the Project of School-enterprise Cooperative Practice Education Base [Grant Number 2022xqhzsjjd01], the Anhui Provincial Humanities and Social Science Foundation of China [Grant Numbers SK2020A0380, SK20210466, SK2021A0468], the Key Project of Anhui University Outstanding Young Talents Support Plan [Grant Numbers gxyqZD2016180]; Wannan Medical College teaching Quality and Teaching reform project [Grant Numbers 2019ylzy01, 2019kcbz02], the Quality Engineering Teaching Research Project in Wannan Medical College [Grant Numbers 2021ylkc03, 2022jyxm08], and Collaborative Innovation Project of Universities in Anhui Province [Grant Number GXXT-2021-087].

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Authors and Affiliations

  1. Department of Medical Information, Wannan Medical College, Wuhu, 241000, Anhui, China

    Chang Jie

  2. Medical Image Center of Yi-Ji Shan Hospital, Wannan Medical College, Wuhu, 241000, Anhui, China

    Chen Jiming & Shao Ying

  3. College of Mathematics and Statistics, Zhoukou Normal University, Zhoukou, 466000, Henan, China

    Tong Yanchun

  4. Zhejiang Normal University Library, Zhejiang Normal University, Jinhua, 321000, Zhejiang, China

    Ren Haodong

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  1. Chang Jie
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  2. Chen Jiming
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  3. Shao Ying
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  4. Tong Yanchun
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  5. Ren Haodong
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Contributions

JC and HR wrote the main manuscript text; JC and YS prepared the dataset and pre-processed the CXR images; JC and YT designed the proposed model. All authors reviewed the manuscript.

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Correspondence to Tong Yanchun or Ren Haodong.

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Jie, C., Jiming, C., Ying, S. et al. A pyramid GNN model for CXR-based COVID-19 classification. J Supercomput 80, 5490–5508 (2024). https://doi.org/10.1007/s11227-023-05633-1

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