Cloud-Based Framework for COVID-19 Detection through Feature Fusion with Bootstrap Aggregated Extreme Learning Machine
Background. Cloud-based environment for machine learning plays a vital role in medical imaging analysis and predominantly for the people residing in rural areas where health facilities are insufficient. Diagnosis of COVID-19 based on machine learning with cloud computing act to assist radiologists a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/3111200 |
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| _version_ | 1832553583429550080 |
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| author | Amjad Rehman Khan Tanzila Saba Tariq Sadad Seng-phil Hong |
| author_facet | Amjad Rehman Khan Tanzila Saba Tariq Sadad Seng-phil Hong |
| author_sort | Amjad Rehman Khan |
| collection | DOAJ |
| description | Background. Cloud-based environment for machine learning plays a vital role in medical imaging analysis and predominantly for the people residing in rural areas where health facilities are insufficient. Diagnosis of COVID-19 based on machine learning with cloud computing act to assist radiologists and support telehealth services for remote diagnostics during this pandemic. Methods. In the proposed computer-aided diagnosis (CAD) system, the balance contrast enhancement technique (BCET) is utilized to enhance the chest X-ray images. Textural and shape-based features are extracted from the preprocessed X-ray images, and the fusion of these features generates the final feature vector. The gain ratio is applied for feature selection to remove insignificant features. An extreme learning machine (ELM) is a neural network modification with a high capability for pattern recognition and classification problems for COVID-19 detection. Results. However, to further improve the accuracy of ELM, we proposed bootstrap aggregated extreme learning machine (BA-ELM). The proposed cloud-based model is evaluated on a benchmark dataset COVID-Xray-5k dataset. We choose 504 (after data augmentation) and 100 images of COVID-19 for training and testing, respectively. Conclusion. Finally, 2000 and 1000 images are selected from the non-COVID-19 category for training and testing. The model achieved an average accuracy of 95.7%. |
| format | Article |
| id | doaj-art-0517ea71001144dbbe91446d8258d535 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-0517ea71001144dbbe91446d8258d5352025-02-03T05:53:38ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/3111200Cloud-Based Framework for COVID-19 Detection through Feature Fusion with Bootstrap Aggregated Extreme Learning MachineAmjad Rehman Khan0Tanzila Saba1Tariq Sadad2Seng-phil Hong3Artificial Intelligence & Data Analytics Research Lab (AIDA)Artificial Intelligence & Data Analytics Research Lab (AIDA)Department of Computer Science & Software EngineeringHANCOMBackground. Cloud-based environment for machine learning plays a vital role in medical imaging analysis and predominantly for the people residing in rural areas where health facilities are insufficient. Diagnosis of COVID-19 based on machine learning with cloud computing act to assist radiologists and support telehealth services for remote diagnostics during this pandemic. Methods. In the proposed computer-aided diagnosis (CAD) system, the balance contrast enhancement technique (BCET) is utilized to enhance the chest X-ray images. Textural and shape-based features are extracted from the preprocessed X-ray images, and the fusion of these features generates the final feature vector. The gain ratio is applied for feature selection to remove insignificant features. An extreme learning machine (ELM) is a neural network modification with a high capability for pattern recognition and classification problems for COVID-19 detection. Results. However, to further improve the accuracy of ELM, we proposed bootstrap aggregated extreme learning machine (BA-ELM). The proposed cloud-based model is evaluated on a benchmark dataset COVID-Xray-5k dataset. We choose 504 (after data augmentation) and 100 images of COVID-19 for training and testing, respectively. Conclusion. Finally, 2000 and 1000 images are selected from the non-COVID-19 category for training and testing. The model achieved an average accuracy of 95.7%.http://dx.doi.org/10.1155/2022/3111200 |
| spellingShingle | Amjad Rehman Khan Tanzila Saba Tariq Sadad Seng-phil Hong Cloud-Based Framework for COVID-19 Detection through Feature Fusion with Bootstrap Aggregated Extreme Learning Machine Discrete Dynamics in Nature and Society |
| title | Cloud-Based Framework for COVID-19 Detection through Feature Fusion with Bootstrap Aggregated Extreme Learning Machine |
| title_full | Cloud-Based Framework for COVID-19 Detection through Feature Fusion with Bootstrap Aggregated Extreme Learning Machine |
| title_fullStr | Cloud-Based Framework for COVID-19 Detection through Feature Fusion with Bootstrap Aggregated Extreme Learning Machine |
| title_full_unstemmed | Cloud-Based Framework for COVID-19 Detection through Feature Fusion with Bootstrap Aggregated Extreme Learning Machine |
| title_short | Cloud-Based Framework for COVID-19 Detection through Feature Fusion with Bootstrap Aggregated Extreme Learning Machine |
| title_sort | cloud based framework for covid 19 detection through feature fusion with bootstrap aggregated extreme learning machine |
| url | http://dx.doi.org/10.1155/2022/3111200 |
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