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. 2022 Sep 20;11(19):5501.
doi: 10.3390/jcm11195501.

A Novel Lightweight Approach to COVID-19 Diagnostics Based on Chest X-ray Images

Agata Giełczyk  1 , Anna Marciniak  1   2 , Martyna Tarczewska  1 , Sylwester Michal Kloska  2 , Alicja Harmoza  2 , Zbigniew Serafin  2 , Marcin Woźniak  2
Affiliations

A Novel Lightweight Approach to COVID-19 Diagnostics Based on Chest X-ray Images

Agata Giełczyk et al. J Clin Med. .

Abstract

Background: This paper presents a novel lightweight approach based on machine learning methods supporting COVID-19 diagnostics based on X-ray images. The presented schema offers effective and quick diagnosis of COVID-19.

Methods: Real data (X-ray images) from hospital patients were used in this study. All labels, namely those that were COVID-19 positive and negative, were confirmed by a PCR test. Feature extraction was performed using a convolutional neural network, and the subsequent classification of samples used Random Forest, XGBoost, LightGBM and CatBoost.

Results: The LightGBM model was the most effective in classifying patients on the basis of features extracted from X-ray images, with an accuracy of 1.00, a precision of 1.00, a recall of 1.00 and an F1-score of 1.00.

Conclusion: The proposed schema can potentially be used as a support for radiologists to improve the diagnostic process. The presented approach is efficient and fast. Moreover, it is not excessively complex computationally.

Keywords: COVID-19; X-ray images; features extraction; image processing; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The following steps of processing in the proposed method: data acquisition, data augmentation, sample pre-processing, features extraction and binary classification of COVID-19 as positive or negative (healthy).
Figure 2
Figure 2
The exemplary images from the dataset divided into two classes: Healthy and COVID-19 confirmed by a PCR test.
Figure 3
Figure 3
The architecture of the CNN used in the research. In dashed lines, the added Dense network was in solely a CNN-based approach.
See this image and copyright information in PMC

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