Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease
from X-ray Images
- URL: http://arxiv.org/abs/2006.13817v1
- Date: Mon, 22 Jun 2020 17:55:16 GMT
- Title: Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease
from X-ray Images
- Authors: Mahesh Gour, Sweta Jain
- Abstract summary: We design a new stacked convolutional neural network model for the automatic diagnosis of COVID-19 disease from the chest X-ray images.
The proposed CNN model combines the discriminating power of the different CNNs sub-models and classifies chest X-ray images into COVID-19, Normal, and Pneumonia classes.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
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