Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features
Chest X-ray image contains sufficient information that finds wide-spread applications in diverse disease diagnosis and decision making to assist the medical experts. This paper has proposed an intelligent approach to detect Covid-19 from the chest X-ray image using the hybridization of deep convolut...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820306182 |
| _version_ | 1818251386348896256 |
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| author | Rafid Mostafiz Mohammad Shorif Uddin Nur-A- Alam Md. Mahfuz Reza Mohammad Motiur Rahman |
| author_facet | Rafid Mostafiz Mohammad Shorif Uddin Nur-A- Alam Md. Mahfuz Reza Mohammad Motiur Rahman |
| author_sort | Rafid Mostafiz |
| collection | DOAJ |
| description | Chest X-ray image contains sufficient information that finds wide-spread applications in diverse disease diagnosis and decision making to assist the medical experts. This paper has proposed an intelligent approach to detect Covid-19 from the chest X-ray image using the hybridization of deep convolutional neural network (CNN) and discrete wavelet transform (DWT) features. At first, the X-ray image is enhanced and segmented through preprocessing tasks, and then deep CNN and DWT features are extracted. The optimum features are extracted from these hybridized features through minimum redundancy and maximum relevance (mRMR) along with recursive feature elimination (RFE). Finally, the random forest-based bagging approach is used for doing the detection task. An extensive experiment is performed, and the results confirm that our approach gives satisfactory performance compare to the existing methods with an overall accuracy of more than 98.5%. |
| first_indexed | 2024-12-12T16:07:27Z |
| format | Article |
| id | doaj.art-6951550531f945c492d02db3950ea657 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-12-12T16:07:27Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-6951550531f945c492d02db3950ea6572022-12-22T00:19:16ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-06-0134632263235Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized featuresRafid Mostafiz0Mohammad Shorif Uddin1Nur-A- Alam2Md. Mahfuz Reza3Mohammad Motiur Rahman4Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Bangladesh; Department of Computer Science and Engineering, Dhaka International University, BangladeshDepartment of Computer Science and Engineering, Jahangirnagar University, Bangladesh; Corresponding author at: Department of Computer Science and Engineering, Jahangirnagar University, Bangladesh.Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Bangladesh; Department of Computer Science and Engineering, Dhaka International University, BangladeshDepartment of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, BangladeshDepartment of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, BangladeshChest X-ray image contains sufficient information that finds wide-spread applications in diverse disease diagnosis and decision making to assist the medical experts. This paper has proposed an intelligent approach to detect Covid-19 from the chest X-ray image using the hybridization of deep convolutional neural network (CNN) and discrete wavelet transform (DWT) features. At first, the X-ray image is enhanced and segmented through preprocessing tasks, and then deep CNN and DWT features are extracted. The optimum features are extracted from these hybridized features through minimum redundancy and maximum relevance (mRMR) along with recursive feature elimination (RFE). Finally, the random forest-based bagging approach is used for doing the detection task. An extensive experiment is performed, and the results confirm that our approach gives satisfactory performance compare to the existing methods with an overall accuracy of more than 98.5%.http://www.sciencedirect.com/science/article/pii/S1319157820306182Covid-19Convolutional neural network (CNN)Discrete wavelet transform (DWT)Minimum redundancy maximum relevance (mRMR)Recursive feature elimination (RFE)Random forest classifier |
| spellingShingle | Rafid Mostafiz Mohammad Shorif Uddin Nur-A- Alam Md. Mahfuz Reza Mohammad Motiur Rahman Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features Journal of King Saud University: Computer and Information Sciences Covid-19 Convolutional neural network (CNN) Discrete wavelet transform (DWT) Minimum redundancy maximum relevance (mRMR) Recursive feature elimination (RFE) Random forest classifier |
| title | Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features |
| title_full | Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features |
| title_fullStr | Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features |
| title_full_unstemmed | Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features |
| title_short | Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features |
| title_sort | covid 19 detection in chest x ray through random forest classifier using a hybridization of deep cnn and dwt optimized features |
| topic | Covid-19 Convolutional neural network (CNN) Discrete wavelet transform (DWT) Minimum redundancy maximum relevance (mRMR) Recursive feature elimination (RFE) Random forest classifier |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820306182 |
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