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Article

MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN Models

by
Thanakorn Phumkuea
1,*,†,
Thakerng Wongsirichot
2,*,†,
Kasikrit Damkliang
2,
Asma Navasakulpong
3 and
Jarutas Andritsch
4
1
College of Digital Science, Prince of Songkla University, Songkhla 90110, Thailand
2
Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla 90110, Thailand
3
Division of Respiratory and Respiratory Critical Care Medicine, Prince of Songkla University, Songkhla 90110, Thailand
4
Faculty of Business, Law and Digital Technologies, Solent University, Southampton SO14 0YN, UK
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Tomography 2023, 9(6), 2233-2246; https://doi.org/10.3390/tomography9060173
Submission received: 2 November 2023 / Revised: 8 December 2023 / Accepted: 8 December 2023 / Published: 13 December 2023
(This article belongs to the Special Issue The Challenge of Advanced Medical Imaging Data Analysis in COVID-19)
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Abstract

This study introduces a Multi-Stage Automated Classification (MSTAC) system for COVID-19 chest X-ray (CXR) images, utilizing stacked Convolutional Neural Network (CNN) models. Suspected COVID-19 patients often undergo CXR imaging, making it valuable for disease classification. The study collected CXR images from public datasets and aimed to differentiate between COVID-19, non-COVID-19, and healthy cases. MSTAC employs two classification stages: the first distinguishes healthy from unhealthy cases, and the second further classifies COVID-19 and non-COVID-19 cases. Compared to a single CNN-Multiclass model, MSTAC demonstrated superior classification performance, achieving 97.30% accuracy and sensitivity. In contrast, the CNN-Multiclass model showed 94.76% accuracy and sensitivity. MSTAC’s effectiveness is highlighted in its promising results over the CNN-Multiclass model, suggesting its potential to assist healthcare professionals in efficiently diagnosing COVID-19 cases. The system outperformed similar techniques, emphasizing its accuracy and efficiency in COVID-19 diagnosis. This research underscores MSTAC as a valuable tool in medical image analysis for enhanced disease classification.
Keywords: COVID-19; CXR; deep learning; CNN; multiclass model COVID-19; CXR; deep learning; CNN; multiclass model

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MDPI and ACS Style

Phumkuea, T.; Wongsirichot, T.; Damkliang, K.; Navasakulpong, A.; Andritsch, J. MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN Models. Tomography 2023, 9, 2233-2246. https://doi.org/10.3390/tomography9060173

AMA Style

Phumkuea T, Wongsirichot T, Damkliang K, Navasakulpong A, Andritsch J. MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN Models. Tomography. 2023; 9(6):2233-2246. https://doi.org/10.3390/tomography9060173

Chicago/Turabian Style

Phumkuea, Thanakorn, Thakerng Wongsirichot, Kasikrit Damkliang, Asma Navasakulpong, and Jarutas Andritsch. 2023. "MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN Models" Tomography 9, no. 6: 2233-2246. https://doi.org/10.3390/tomography9060173

APA Style

Phumkuea, T., Wongsirichot, T., Damkliang, K., Navasakulpong, A., & Andritsch, J. (2023). MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN Models. Tomography, 9(6), 2233-2246. https://doi.org/10.3390/tomography9060173

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