CXGNet: A tri-phase chest X-ray image classification for COVID-19 diagnosis using deep CNN with enhanced grey-wolf optimizer.

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Abstract

The coronavirus disease 2019 (COVID-19) epidemic had a significant impact on daily life in many nations and global public health. COVID’s quick spread has become one of the biggest disruptive calamities in the world. In the fight against COVID-19, it’s critical to keep a close eye on the initial stage of infection in patients. Furthermore, early COVID-19 discovery by precise diagnosis, especially in patients with no evident symptoms, may reduce the patient’s death rate and can stop the spread of COVID-19. When compared to CT images, chest X-ray (CXR) images are now widely employed for COVID-19 diagnosis since CXR images contain more robust features of the lung. Furthermore, radiologists can easily diagnose CXR images because of its operating speed and low cost, and it is promising for emergency situations and therapy. This work proposes a tri-stage CXR image based COVID-19 classification model using deep learning convolutional neural networks (DLCNN) with an optimal feature selection technique named as enhanced grey-wolf optimizer with genetic algorithm (EGWO-GA), which is denoted as CXGNet. The proposed CXGNet is implemented as multiple classes, such as 4-class, 3-class, and 2-class models based on the diseases. Extensive simulation outcome discloses the superiority of the proposed CXGNet model with enhanced classification accuracy of 94.00% for the 4-class model, 97.05% of accuracy for the 3-class model, and 100% accuracy for the 2-class model as compared to conventional methods.© 2022 Elsevier Ltd. All rights reserved.