Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation
BackgroundThe COVID-19 pandemic has raised global concern, with moderate to severe cases displaying lung inflammation and respiratory failure. Chest x-ray (CXR) imaging is crucial for diagnosis and is usually interpreted by experienced medical specialists. Machine learning ha...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-02-01
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| Series: | JMIR Formative Research |
| Online Access: | https://formative.jmir.org/2023/1/e42324 |
| _version_ | 1797734333038985216 |
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| author | Thanakorn Phumkuea Thakerng Wongsirichot Kasikrit Damkliang Asma Navasakulpong |
| author_facet | Thanakorn Phumkuea Thakerng Wongsirichot Kasikrit Damkliang Asma Navasakulpong |
| author_sort | Thanakorn Phumkuea |
| collection | DOAJ |
| description |
BackgroundThe COVID-19 pandemic has raised global concern, with moderate to severe cases displaying lung inflammation and respiratory failure. Chest x-ray (CXR) imaging is crucial for diagnosis and is usually interpreted by experienced medical specialists. Machine learning has been applied with acceptable accuracy, but computational efficiency has received less attention.
ObjectiveWe introduced a novel hybrid machine learning model to accurately classify COVID-19, non-COVID-19, and healthy patients from CXR images with reduced computational time and promising results. Our proposed model was thoroughly evaluated and compared with existing models.
MethodsA retrospective study was conducted to analyze 5 public data sets containing 4200 CXR images using machine learning techniques including decision trees, support vector machines, and neural networks. The images were preprocessed to undergo image segmentation, enhancement, and feature extraction. The best performing machine learning technique was selected and combined into a multilayer hybrid classification model for COVID-19 (MLHC-COVID-19). The model consisted of 2 layers. The first layer was designed to differentiate healthy individuals from infected patients, while the second layer aimed to classify COVID-19 and non-COVID-19 patients.
ResultsThe MLHC-COVID-19 model was trained and evaluated on unseen COVID-19 CXR images, achieving reasonably high accuracy and F measures of 0.962 and 0.962, respectively. These results show the effectiveness of the MLHC-COVID-19 in classifying COVID-19 CXR images, with improved accuracy and a reduction in interpretation time. The model was also embedded into a web-based MLHC-COVID-19 computer-aided diagnosis system, which was made publicly available.
ConclusionsThe study found that the MLHC-COVID-19 model effectively differentiated CXR images of COVID-19 patients from those of healthy and non-COVID-19 individuals. It outperformed other state-of-the-art deep learning techniques and showed promising results. These results suggest that the MLHC-COVID-19 model could have been instrumental in early detection and diagnosis of COVID-19 patients, thus playing a significant role in controlling and managing the pandemic. Although the pandemic has slowed down, this model can be adapted and utilized for future similar situations. The model was also integrated into a publicly accessible web-based computer-aided diagnosis system. |
| first_indexed | 2024-03-12T12:41:47Z |
| format | Article |
| id | doaj.art-ca5a3c29debe4b4195abd0f903ca3556 |
| institution | Directory Open Access Journal |
| issn | 2561-326X |
| language | English |
| last_indexed | 2024-03-12T12:41:47Z |
| publishDate | 2023-02-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Formative Research |
| spelling | doaj.art-ca5a3c29debe4b4195abd0f903ca35562023-08-28T23:47:43ZengJMIR PublicationsJMIR Formative Research2561-326X2023-02-017e4232410.2196/42324Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and EvaluationThanakorn Phumkueahttps://orcid.org/0000-0001-9078-5689Thakerng Wongsirichothttps://orcid.org/0000-0003-2054-3742Kasikrit Damklianghttps://orcid.org/0000-0002-5342-7302Asma Navasakulponghttps://orcid.org/0000-0003-1442-1204 BackgroundThe COVID-19 pandemic has raised global concern, with moderate to severe cases displaying lung inflammation and respiratory failure. Chest x-ray (CXR) imaging is crucial for diagnosis and is usually interpreted by experienced medical specialists. Machine learning has been applied with acceptable accuracy, but computational efficiency has received less attention. ObjectiveWe introduced a novel hybrid machine learning model to accurately classify COVID-19, non-COVID-19, and healthy patients from CXR images with reduced computational time and promising results. Our proposed model was thoroughly evaluated and compared with existing models. MethodsA retrospective study was conducted to analyze 5 public data sets containing 4200 CXR images using machine learning techniques including decision trees, support vector machines, and neural networks. The images were preprocessed to undergo image segmentation, enhancement, and feature extraction. The best performing machine learning technique was selected and combined into a multilayer hybrid classification model for COVID-19 (MLHC-COVID-19). The model consisted of 2 layers. The first layer was designed to differentiate healthy individuals from infected patients, while the second layer aimed to classify COVID-19 and non-COVID-19 patients. ResultsThe MLHC-COVID-19 model was trained and evaluated on unseen COVID-19 CXR images, achieving reasonably high accuracy and F measures of 0.962 and 0.962, respectively. These results show the effectiveness of the MLHC-COVID-19 in classifying COVID-19 CXR images, with improved accuracy and a reduction in interpretation time. The model was also embedded into a web-based MLHC-COVID-19 computer-aided diagnosis system, which was made publicly available. ConclusionsThe study found that the MLHC-COVID-19 model effectively differentiated CXR images of COVID-19 patients from those of healthy and non-COVID-19 individuals. It outperformed other state-of-the-art deep learning techniques and showed promising results. These results suggest that the MLHC-COVID-19 model could have been instrumental in early detection and diagnosis of COVID-19 patients, thus playing a significant role in controlling and managing the pandemic. Although the pandemic has slowed down, this model can be adapted and utilized for future similar situations. The model was also integrated into a publicly accessible web-based computer-aided diagnosis system.https://formative.jmir.org/2023/1/e42324 |
| spellingShingle | Thanakorn Phumkuea Thakerng Wongsirichot Kasikrit Damkliang Asma Navasakulpong Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation JMIR Formative Research |
| title | Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation |
| title_full | Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation |
| title_fullStr | Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation |
| title_full_unstemmed | Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation |
| title_short | Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation |
| title_sort | classifying covid 19 patients from chest x ray images using hybrid machine learning techniques development and evaluation |
| url | https://formative.jmir.org/2023/1/e42324 |
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