About: Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease from X-ray Images

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease from X-ray Images
Creator	Gour, Mahesh Jain, Sweta
source	ArXiv
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.
has issue date	2020-06-22(xsd:dateTime)
has license	arxiv
sha1sum (hex)	1d44f8fe2486ee551dfd4930b1afb4b7b179795c
resource representing a document's title	Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease from X-ray Images
resource representing a document's body	covid:1d44f8fe2486ee551dfd4930b1afb4b7b179795c#body_text
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