COVID-19 detection from chest CT images using optimized deep features and ensemble classification
Diagnosis of COVID-19 positive patients is the eventual move to impede the expansion of coronavirus. Variations of coronavirus make it tough to recognize COVID-19 positive patients through symptoms. Hence, this research aims at a faster and automatic detection approach of COVID-19 disease from the c...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924000061 |
| _version_ | 1797317479102414848 |
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| author | Muhammad Minoar Hossain Md. Abul Ala Walid S.M. Saklain Galib Mir Mohammad Azad Wahidur Rahman A.S.M. Shafi Mohammad Motiur Rahman |
| author_facet | Muhammad Minoar Hossain Md. Abul Ala Walid S.M. Saklain Galib Mir Mohammad Azad Wahidur Rahman A.S.M. Shafi Mohammad Motiur Rahman |
| author_sort | Muhammad Minoar Hossain |
| collection | DOAJ |
| description | Diagnosis of COVID-19 positive patients is the eventual move to impede the expansion of coronavirus. Variations of coronavirus make it tough to recognize COVID-19 positive patients through symptoms. Hence, this research aims at a faster and automatic detection approach of COVID-19 disease from the chest Computed tomography (CT) scan images. For the composition of the system, this approach constructs a feature vector from the CT images through the features fusion of two Convolutional neural network (CNN) models namely VGG-19 and ResNet-50. Before the feature fusion, preprocessing techniques are applied to gain more accurate outcomes. Moreover, pertinent features are identified from the feature vector by using several feature optimization methods namely Recursive feature elimination (RFE), Principal component analysis (PCA), and Linear discriminant analysis (LDA), and among them, we have observed PCA as the best preference. Classification is performed on the optimized feature utilizing the Max voting ensemble classification (MVEC). The fused features of VGG-19 and ResNet-50, processed with PCA and MVEC, provide the best outcomes of accuracy, specificity, sensitivity, and precision at 98.51 %, 97.58 %, 99.49 %, and 97.47 %, respectively, after 5-fold cross-validation for the proposed method. |
| first_indexed | 2024-03-08T03:35:37Z |
| format | Article |
| id | doaj.art-c5d181f6c6694f24a92ea6165fd7d1a5 |
| institution | Directory Open Access Journal |
| issn | 2772-9419 |
| language | English |
| last_indexed | 2024-03-08T03:35:37Z |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj.art-c5d181f6c6694f24a92ea6165fd7d1a52024-02-10T04:45:49ZengElsevierSystems and Soft Computing2772-94192024-12-016200077COVID-19 detection from chest CT images using optimized deep features and ensemble classificationMuhammad Minoar Hossain0Md. Abul Ala Walid1S.M. Saklain Galib2Mir Mohammad Azad3Wahidur Rahman4A.S.M. Shafi5Mohammad Motiur Rahman6Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Bangladesh; Department of Computer Science and Engineering, Bangladesh University, Dhaka, Bangladesh; Corresponding author at: Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh.Department of Data Science, Bangabandhu Sheikh Mujibur Rahman Digital University, Kaliakair, Gazipur-1750, BangladeshDepartment of Biomedical Engineering, Khulna University of Engineering and Technology (KUET), Khulna, 9203, BangladeshIndependent researcher, Dhaka, BangladeshDepartment of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Bangladesh; Department of Computer Science and Engineering, Uttara University, Dhaka, 1230, BangladeshDepartment of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, BangladeshDepartment of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, BangladeshDiagnosis of COVID-19 positive patients is the eventual move to impede the expansion of coronavirus. Variations of coronavirus make it tough to recognize COVID-19 positive patients through symptoms. Hence, this research aims at a faster and automatic detection approach of COVID-19 disease from the chest Computed tomography (CT) scan images. For the composition of the system, this approach constructs a feature vector from the CT images through the features fusion of two Convolutional neural network (CNN) models namely VGG-19 and ResNet-50. Before the feature fusion, preprocessing techniques are applied to gain more accurate outcomes. Moreover, pertinent features are identified from the feature vector by using several feature optimization methods namely Recursive feature elimination (RFE), Principal component analysis (PCA), and Linear discriminant analysis (LDA), and among them, we have observed PCA as the best preference. Classification is performed on the optimized feature utilizing the Max voting ensemble classification (MVEC). The fused features of VGG-19 and ResNet-50, processed with PCA and MVEC, provide the best outcomes of accuracy, specificity, sensitivity, and precision at 98.51 %, 97.58 %, 99.49 %, and 97.47 %, respectively, after 5-fold cross-validation for the proposed method.http://www.sciencedirect.com/science/article/pii/S2772941924000061Feature optimizationFusionEnsembleClassification |
| spellingShingle | Muhammad Minoar Hossain Md. Abul Ala Walid S.M. Saklain Galib Mir Mohammad Azad Wahidur Rahman A.S.M. Shafi Mohammad Motiur Rahman COVID-19 detection from chest CT images using optimized deep features and ensemble classification Systems and Soft Computing Feature optimization Fusion Ensemble Classification |
| title | COVID-19 detection from chest CT images using optimized deep features and ensemble classification |
| title_full | COVID-19 detection from chest CT images using optimized deep features and ensemble classification |
| title_fullStr | COVID-19 detection from chest CT images using optimized deep features and ensemble classification |
| title_full_unstemmed | COVID-19 detection from chest CT images using optimized deep features and ensemble classification |
| title_short | COVID-19 detection from chest CT images using optimized deep features and ensemble classification |
| title_sort | covid 19 detection from chest ct images using optimized deep features and ensemble classification |
| topic | Feature optimization Fusion Ensemble Classification |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924000061 |
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