MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN Models
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 CX...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Tomography |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2379-139X/9/6/173 |
| _version_ | 1797379214473691136 |
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| author | Thanakorn Phumkuea Thakerng Wongsirichot Kasikrit Damkliang Asma Navasakulpong Jarutas Andritsch |
| author_facet | Thanakorn Phumkuea Thakerng Wongsirichot Kasikrit Damkliang Asma Navasakulpong Jarutas Andritsch |
| author_sort | Thanakorn Phumkuea |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T20:18:55Z |
| format | Article |
| id | doaj.art-39b0b4d6bb184d55bf26af6e0590c5f3 |
| institution | Directory Open Access Journal |
| issn | 2379-1381 2379-139X |
| language | English |
| last_indexed | 2024-03-08T20:18:55Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Tomography |
| spelling | doaj.art-39b0b4d6bb184d55bf26af6e0590c5f32023-12-22T14:45:52ZengMDPI AGTomography2379-13812379-139X2023-12-01962233224610.3390/tomography9060173MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN ModelsThanakorn Phumkuea0Thakerng Wongsirichot1Kasikrit Damkliang2Asma Navasakulpong3Jarutas Andritsch4College of Digital Science, Prince of Songkla University, Songkhla 90110, ThailandDivision of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla 90110, ThailandDivision of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla 90110, ThailandDivision of Respiratory and Respiratory Critical Care Medicine, Prince of Songkla University, Songkhla 90110, ThailandFaculty of Business, Law and Digital Technologies, Solent University, Southampton SO14 0YN, UKThis 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.https://www.mdpi.com/2379-139X/9/6/173COVID-19CXRdeep learningCNNmulticlass model |
| spellingShingle | Thanakorn Phumkuea Thakerng Wongsirichot Kasikrit Damkliang Asma Navasakulpong Jarutas Andritsch MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN Models Tomography COVID-19 CXR deep learning CNN multiclass model |
| title | MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN Models |
| title_full | MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN Models |
| title_fullStr | MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN Models |
| title_full_unstemmed | MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN Models |
| title_short | MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN Models |
| title_sort | mstac a multi stage automated classification of covid 19 chest x ray images using stacked cnn models |
| topic | COVID-19 CXR deep learning CNN multiclass model |
| url | https://www.mdpi.com/2379-139X/9/6/173 |
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