Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN
Abstract The COVID-19 disease caused by coronavirus is constantly changing due to the emergence of different variants and thousands of people are dying every day worldwide. Early detection of this new form of pulmonary disease can reduce the mortality rate. In this paper, an automated method based o...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-47183-9 |
| _version_ | 1797559549342777344 |
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| author | Md. Nur-A-Alam Mostofa Kamal Nasir Mominul Ahsan Md Abdul Based Julfikar Haider Marcin Kowalski |
| author_facet | Md. Nur-A-Alam Mostofa Kamal Nasir Mominul Ahsan Md Abdul Based Julfikar Haider Marcin Kowalski |
| author_sort | Md. Nur-A-Alam |
| collection | DOAJ |
| description | Abstract The COVID-19 disease caused by coronavirus is constantly changing due to the emergence of different variants and thousands of people are dying every day worldwide. Early detection of this new form of pulmonary disease can reduce the mortality rate. In this paper, an automated method based on machine learning (ML) and deep learning (DL) has been developed to detect COVID-19 using computed tomography (CT) scan images extracted from three publicly available datasets (A total of 11,407 images; 7397 COVID-19 images and 4010 normal images). An unsupervised clustering approach that is a modified region-based clustering technique for segmenting COVID-19 CT scan image has been proposed. Furthermore, contourlet transform and convolution neural network (CNN) have been employed to extract features individually from the segmented CT scan images and to fuse them in one feature vector. Binary differential evolution (BDE) approach has been employed as a feature optimization technique to obtain comprehensible features from the fused feature vector. Finally, a ML/DL-based ensemble classifier considering bagging technique has been employed to detect COVID-19 from the CT images. A fivefold and generalization cross-validation techniques have been used for the validation purpose. Classification experiments have also been conducted with several pre-trained models (AlexNet, ResNet50, GoogleNet, VGG16, VGG19) and found that the ensemble classifier technique with fused feature has provided state-of-the-art performance with an accuracy of 99.98%. |
| first_indexed | 2024-03-10T17:46:58Z |
| format | Article |
| id | doaj.art-7797879344d54b61bf5e8b1710d5309b |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T17:46:58Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7797879344d54b61bf5e8b1710d5309b2023-11-20T09:30:12ZengNature PortfolioScientific Reports2045-23222023-11-0113112110.1038/s41598-023-47183-9Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNNMd. Nur-A-Alam0Mostofa Kamal Nasir1Mominul Ahsan2Md Abdul Based3Julfikar Haider4Marcin Kowalski5Department of Computer Science & Engineering, Mawlana Bhashani Science and Technology UniversityDepartment of Computer Science & Engineering, Mawlana Bhashani Science and Technology UniversityDepartment of Computer Science, University of YorkDepartment of Computer Science & Engineering, Dhaka International UniversityDepartment of Engineering, Manchester Metropolitan UniversityInstitute of Optoelectronics, Military University of TechnologyAbstract The COVID-19 disease caused by coronavirus is constantly changing due to the emergence of different variants and thousands of people are dying every day worldwide. Early detection of this new form of pulmonary disease can reduce the mortality rate. In this paper, an automated method based on machine learning (ML) and deep learning (DL) has been developed to detect COVID-19 using computed tomography (CT) scan images extracted from three publicly available datasets (A total of 11,407 images; 7397 COVID-19 images and 4010 normal images). An unsupervised clustering approach that is a modified region-based clustering technique for segmenting COVID-19 CT scan image has been proposed. Furthermore, contourlet transform and convolution neural network (CNN) have been employed to extract features individually from the segmented CT scan images and to fuse them in one feature vector. Binary differential evolution (BDE) approach has been employed as a feature optimization technique to obtain comprehensible features from the fused feature vector. Finally, a ML/DL-based ensemble classifier considering bagging technique has been employed to detect COVID-19 from the CT images. A fivefold and generalization cross-validation techniques have been used for the validation purpose. Classification experiments have also been conducted with several pre-trained models (AlexNet, ResNet50, GoogleNet, VGG16, VGG19) and found that the ensemble classifier technique with fused feature has provided state-of-the-art performance with an accuracy of 99.98%.https://doi.org/10.1038/s41598-023-47183-9 |
| spellingShingle | Md. Nur-A-Alam Mostofa Kamal Nasir Mominul Ahsan Md Abdul Based Julfikar Haider Marcin Kowalski Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN Scientific Reports |
| title | Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN |
| title_full | Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN |
| title_fullStr | Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN |
| title_full_unstemmed | Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN |
| title_short | Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN |
| title_sort | ensemble classification of integrated ct scan datasets in detecting covid 19 using feature fusion from contourlet transform and cnn |
| url | https://doi.org/10.1038/s41598-023-47183-9 |
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