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

TL;DR

This paper introduces a novel stacked CNN model that combines multiple sub-models to accurately diagnose COVID-19 from chest X-ray images, achieving high accuracy and outperforming existing methods.

Contribution

The paper presents a new stacked CNN architecture that integrates sub-models from VGG19 and a custom 30-layer CNN for improved COVID-19 diagnosis from X-ray images.

Findings

Achieved 92.74% accuracy in COVID-19 classification.

Demonstrated superior performance over existing methods.

Created a new dataset with 2764 X-ray images for research.

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.