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• W3037649076 abstract "Automatic and rapid screening of COVID-19 from the chest X-ray images has become an urgent need in this pandemic situation of SARS-CoV-2 worldwide in 2020. However, accurate and reliable screening of patients is a massive challenge due to the discrepancy between COVID-19 and other viral pneumonia in X-ray images. 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 images. We obtain different sub-models from the VGG19 and developed a 30-layered CNN model (named as CovNet30) during the training, and obtained sub-models are stacked together using logistic regression. The proposed CNN model combines the discriminating power of the different CNN`s sub-models and classifies chest X-ray images into COVID-19, Normal, and Pneumonia classes. In addition, we generate X-ray images dataset referred to as COVID19CXr, which includes 2764 chest x-ray images of 1768 patients from the three publicly available data repositories. The proposed stacked CNN achieves an accuracy of 92.74%, the sensitivity of 93.33%, PPV of 92.13%, and F1-score of 0.93 for the classification of X-ray images. Our proposed approach shows its superiority over the existing methods for the diagnosis of the COVID-19 from the X-ray images." @default.
• W3037649076 created "2020-07-02" @default.
• W3037649076 creator A5043071085 @default.
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• W3037649076 date "2020-06-22" @default.
• W3037649076 modified "2023-09-27" @default.
• W3037649076 title "Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease from X-ray Images." @default.
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