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. 2021 Nov 30:183:115452.
doi: 10.1016/j.eswa.2021.115452. Epub 2021 Jun 22.

Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost

Domingos Alves Dias Júnior  1 , Luana Batista da Cruz  1 , João Otávio Bandeira Diniz  1   2 , Giovanni Lucca França da Silva  1 , Geraldo Braz Junior  1 , Aristófanes Corrêa Silva  1 , Anselmo Cardoso de Paiva  1 , Rodolfo Acatauassú Nunes  3 , Marcelo Gattass  4
Affiliations

Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost

Domingos Alves Dias Júnior et al. Expert Syst Appl. .

Abstract

The COVID-19 pandemic, which originated in December 2019 in the city of Wuhan, China, continues to have a devastating effect on the health and well-being of the global population. Currently, approximately 8.8 million people have already been infected and more than 465,740 people have died worldwide. An important step in combating COVID-19 is the screening of infected patients using chest X-ray (CXR) images. However, this task is extremely time-consuming and prone to variability among specialists owing to its heterogeneity. Therefore, the present study aims to assist specialists in identifying COVID-19 patients from their chest radiographs, using automated computational techniques. The proposed method has four main steps: (1) the acquisition of the dataset, from two public databases; (2) the standardization of images through preprocessing; (3) the extraction of features using a deep features-based approach implemented through the networks VGG19, Inception-v3, and ResNet50; (4) the classifying of images into COVID-19 groups, using eXtreme Gradient Boosting (XGBoost) optimized by particle swarm optimization (PSO). In the best-case scenario, the proposed method achieved an accuracy of 98.71%, a precision of 98.89%, a recall of 99.63%, and an F1-score of 99.25%. In our study, we demonstrated that the problem of classifying CXR images of patients under COVID-19 and non-COVID-19 conditions can be solved efficiently by combining a deep features-based approach with a robust classifier (XGBoost) optimized by an evolutionary algorithm (PSO). The proposed method offers considerable advantages for clinicians seeking to tackle the current COVID-19 pandemic.

Keywords: COVID-19; Chest X-Rays; Deep features; Extreme gradient boosting; Medical images; Particle swarm optimization.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Flowchart of the method.
Fig. 2
Fig. 2
Dataset information: (a) CXR images of patients exhibiting normal conditions; (b) CXR images of patients diagnosed with COVID-19.
Fig. 3
Fig. 3
Preprocessing: (a) input image; (b) color space in grayscale; (c) proportionally resized and centered.
Fig. 4
Fig. 4
CNN Architecture.
Fig. 5
Fig. 5
Activation map for images of patients (a) under normal conditions and (b) affected by COVID-19..
Fig. 6
Fig. 6
Patient affected by COVID-19, wrongly classified by the method.
Fig. 7
Fig. 7
Patient affected by COVID-19 but seemingly normal, classified correctly by the method.
Fig. 8
Fig. 8
Patient affected by COVID-19, classified correctly by the method.
Fig. 9
Fig. 9
Normal patient, classified incorrectly by the method.
Fig. 10
Fig. 10
Normal patient, classified correctly by the method.
Fig. 11
Fig. 11
Activation map for images of patients (a) correctly classified as non-COVID-19 and (b) wrongly classified as COVID-19..
Fig. 12
Fig. 12
Activation map for images of patients (a) correctly classified as non-COVID-19 and (b) wrongly classified as COVID-19..
See this image and copyright information in PMC

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