Enhancing Diagnostic Accuracy with Ensemble Techniques: Detecting COVID-19 and Pneumonia on Chest X-Ray Images
Lung diseases such as COVID-19 and pneumonia can lead to breathing difficulties, decreased lung function, and respiratory failure, leading to death if not treated immediately. Chest X-ray imaging techniques are quick, effective, and inexpensive in controlling many of these diseases. Artificial intel...
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Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT)
2024-04-01
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| Series: | Jordanian Journal of Computers and Information Technology |
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| Online Access: | https://www.jjcit.org/?mno=203230 |
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| author | Fatma A. Mostafa Lamiaa A. Elrefaei Mostafa M. Fouda Aya Hossam |
| author_facet | Fatma A. Mostafa Lamiaa A. Elrefaei Mostafa M. Fouda Aya Hossam |
| author_sort | Fatma A. Mostafa |
| collection | DOAJ |
| description | Lung diseases such as COVID-19 and pneumonia can lead to breathing difficulties, decreased lung function, and respiratory failure, leading to death if not treated immediately. Chest X-ray imaging techniques are quick, effective, and inexpensive in controlling many of these diseases. Artificial intelligence has shown promising results in detecting many diseases, including lung diseases, as it can analyze large sets of data in a short time, which leads to causes reducing the spread of these diseases. Artificial intelligence with the help of deep learning and machine learning is a milestone in modern biomedical research, offering major advances in various medical research disciplines. In this research paper, we introduced various advanced ensemble techniques as bagging, boosting, stacking, and blending with different algorithms, to enhance the performance of our classification models in detecting coronavirus and pneumonia. We specifically focused on combining convolutional neural network (CNN) and vision transformer (ViT) models to create powerful ensemble models. Our objective was to determine the most accurate ensemble technique for diagnosing lung diseases. We assessed their ability to correctly classify chest X-ray images as either COVID-19, pneumonia, or normal. The CatBoost algorithm achieved the highest accuracy of 99.753% using the COVID-19 Radiography dataset and the bagging ensemble model achieved the highest accuracy of 95.08% using 5 different publicly available data (COVIDx CXR-4). The results indicate that the advanced ensemble techniques can significantly improve the performance of machine learning models. [JJCIT 2024; 10(4.000): 428-442] |
| format | Article |
| id | doaj-art-cd0ecfa4bc6a4ccdbf2d8ecd4286feb1 |
| institution | Kabale University |
| issn | 2413-9351 2415-1076 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT) |
| record_format | Article |
| series | Jordanian Journal of Computers and Information Technology |
| spelling | doaj-art-cd0ecfa4bc6a4ccdbf2d8ecd4286feb12024-12-01T05:11:16ZengScientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT)Jordanian Journal of Computers and Information Technology2413-93512415-10762024-04-0110442844210.5455/jjcit.71-1716799666203230Enhancing Diagnostic Accuracy with Ensemble Techniques: Detecting COVID-19 and Pneumonia on Chest X-Ray ImagesFatma A. Mostafa0Lamiaa A. Elrefaei1Mostafa M. Fouda2Aya Hossam3Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, USA Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, EgyptLung diseases such as COVID-19 and pneumonia can lead to breathing difficulties, decreased lung function, and respiratory failure, leading to death if not treated immediately. Chest X-ray imaging techniques are quick, effective, and inexpensive in controlling many of these diseases. Artificial intelligence has shown promising results in detecting many diseases, including lung diseases, as it can analyze large sets of data in a short time, which leads to causes reducing the spread of these diseases. Artificial intelligence with the help of deep learning and machine learning is a milestone in modern biomedical research, offering major advances in various medical research disciplines. In this research paper, we introduced various advanced ensemble techniques as bagging, boosting, stacking, and blending with different algorithms, to enhance the performance of our classification models in detecting coronavirus and pneumonia. We specifically focused on combining convolutional neural network (CNN) and vision transformer (ViT) models to create powerful ensemble models. Our objective was to determine the most accurate ensemble technique for diagnosing lung diseases. We assessed their ability to correctly classify chest X-ray images as either COVID-19, pneumonia, or normal. The CatBoost algorithm achieved the highest accuracy of 99.753% using the COVID-19 Radiography dataset and the bagging ensemble model achieved the highest accuracy of 95.08% using 5 different publicly available data (COVIDx CXR-4). The results indicate that the advanced ensemble techniques can significantly improve the performance of machine learning models. [JJCIT 2024; 10(4.000): 428-442]https://www.jjcit.org/?mno=203230covid-19cxrdensenet-169vit-l32stackingboostingbaggingblending |
| spellingShingle | Fatma A. Mostafa Lamiaa A. Elrefaei Mostafa M. Fouda Aya Hossam Enhancing Diagnostic Accuracy with Ensemble Techniques: Detecting COVID-19 and Pneumonia on Chest X-Ray Images Jordanian Journal of Computers and Information Technology covid-19 cxr densenet-169 vit-l32 stacking boosting bagging blending |
| title | Enhancing Diagnostic Accuracy with Ensemble Techniques: Detecting COVID-19 and Pneumonia on Chest X-Ray Images |
| title_full | Enhancing Diagnostic Accuracy with Ensemble Techniques: Detecting COVID-19 and Pneumonia on Chest X-Ray Images |
| title_fullStr | Enhancing Diagnostic Accuracy with Ensemble Techniques: Detecting COVID-19 and Pneumonia on Chest X-Ray Images |
| title_full_unstemmed | Enhancing Diagnostic Accuracy with Ensemble Techniques: Detecting COVID-19 and Pneumonia on Chest X-Ray Images |
| title_short | Enhancing Diagnostic Accuracy with Ensemble Techniques: Detecting COVID-19 and Pneumonia on Chest X-Ray Images |
| title_sort | enhancing diagnostic accuracy with ensemble techniques detecting covid 19 and pneumonia on chest x ray images |
| topic | covid-19 cxr densenet-169 vit-l32 stacking boosting bagging blending |
| url | https://www.jjcit.org/?mno=203230 |
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