Automated COVID-19 detection from X-ray and CT images with stacked ensemble convolutional neural network

• Autorzy: Gour Mahesh , Jain Sweta

Identyfikatory

DOI

10.1016/j.bbe.2021.12.001

• Języki publikacji: EN

Abstrakty

EN

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 Xray 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.

Słowa kluczowe

EN

COVID-19; automatic screening; stacked ensemble; deep learning; softmax classifier; chest X-ray image; CT scan image

PL

COVID-19; badania przesiewowe; uczenie głębokie; zdjęcie rentgenowskie; tomografia komputerowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 1
• Strony: 27--41
• Opis fizyczny: Bibliogr. 64 poz., rys., tab., wykr.

Twórcy

autor

Gour Mahesh

• Maulana Azad National Institute of Technology, Bhopal, India

autor

Jain Sweta

• Maulana Azad National Institute of Technology, Bhopal, India

Bibliografia

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