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Automatic detection of COVID-19 and pneumonia from chest X-ray images using texture features

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Abstract

COVID-19 has been a devastating pandemic, causing serious and sometimes irreparable damages to body organs. The sooner the existence of this virus in the body is recognized, the more effective the treatments are. This early detection can break the transmission chain faster, reducing the burden of this disease on the society. Since there exist issues regarding the reliability of RT-PCR tests to diagnose COVID-19, examining chest radiographs, especially chest X-ray images, are recommended as well. In this paper, we propose a machine learning algorithm to automatically classify patients in the target groups of COVID-19, pneumonia, and normal, based on chest X-ray images. Our algorithm generates two complementary images from each raw image in the dataset, and only works on a 4-feature vector extracted from the gray level co-occurrence matrix of each image for the classification, based on the k-nearest neighbors algorithm. It can work robustly in the presence of limited data. The speed, simplicity, and the independence from large computational resources are of other advantages of the proposed algorithm. Despite the simplicity and speed, as the results show, the algorithm can compete tightly with the state-of-the-art algorithms.

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Data availability

Datasets used in this study are fully available in [18] and [34]. The source code implemented by the authors is available at Source files. Source files.

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Acknowledgements

The authors would like to express their gratitude to the anonymous reviewers for the valuable comments.

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Authors and Affiliations

  1. Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Farnaz Sheikhi, Aliakbar Taghdiri, Danial Moradisabzevar, Hanieh Rezakhani, Hasti Daneshkia & Mobina Goodarzi

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Contributions

Conceptualization, supervision, methodology, formal analysis, writing-original draft, writing-review & editing: FS; Writing-original draft, writing-review & editing, software, formal analysis, and investigation: AT; Software, formal analysis, investigation, and writing the first draft: DM; Software, formal analysis, investigation, writing the first draft, and reviewing-original draft: HR; Formal analysis, investigation, and writing the first draft: HD; Formal analysis, investigation, and writing the first draft: MG. All authors have read and agreed to this version of the manuscript.

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Correspondence to Farnaz Sheikhi.

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Sheikhi, F., Taghdiri, A., Moradisabzevar, D. et al. Automatic detection of COVID-19 and pneumonia from chest X-ray images using texture features. J Supercomput 79, 21449–21473 (2023). https://doi.org/10.1007/s11227-023-05452-4

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