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Automated COVID-19 detection from X-ray and CT images with stacked ensemble convolutional neural network.
- Source :
- Biocybernetics & Biomedical Engineering; Jan2022, Vol. 42 Issue 1, p27-41, 15p
- Publication Year :
- 2022
-
Abstract
- Automatic and rapid screening of COVID-19 from the radiological (X-ray or CT scan) images has become an urgent need in the current pandemic situation of SARS-CoV-2 worldwide. However, accurate and reliable screening of patients is challenging due to the discrepancy between the radiological images of COVID-19 and other viral pneumonia. So, in this paper, we design a new stacked convolutional neural network model for the automatic diagnosis of COVID-19 disease from the chest X-ray and CT images. In the proposed approach, different sub-models have been obtained from the VGG19 and the Xception models during the training. Thereafter, obtained sub-models are stacked together using softmax classifier. The proposed stacked CNN model combines the discriminating power of the different CNN's sub-models and detects COVID-19 from the radiological images. In addition, we collect CT images to build a CT image dataset and also generate an X-ray images dataset by combining X-ray images from the three publicly available data repositories. The proposed stacked CNN model achieves a sensitivity of 97.62% for the multi-class classification of X-ray images into COVID-19, Normal and Pneumonia Classes and 98.31% sensitivity for binary classification of CT images into COVID-19 and no-Finding classes. Our proposed approach shows superiority over the existing methods for the detection of the COVID-19 cases from the X-ray radiological images. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02085216
- Volume :
- 42
- Issue :
- 1
- Database :
- Supplemental Index
- Journal :
- Biocybernetics & Biomedical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 156588987
- Full Text :
- https://doi.org/10.1016/j.bbe.2021.12.001