Coronavirus disease analysis using chest X-ray images and a novel deep convolutional neural network

https://doi.org/10.1016/j.pdpdt.2021.102473Get rights and content

Highlights

  • A new end-to-end classification technique is proposed for COVID-19 specific pneumonia analysis in chest X-ray images.

  • Two novel CNNs, COVID-RENet-1 and COVID-RENet-2, are developed for COVID-19 detection in chest X-Rays.

  • The proposed COVID-RENets systematically employ Region and Edge-based operations along with convolution.

    The discrimination capacity of the proposed technique is evaluated through extensive extermination and by comparing performance against state-of-the-art CNNs.

Abstract

Background

The recent emergence of a highly infectious and contagious respiratory viral disease known as COVID-19 has vastly impacted human lives and overloaded the health care system. Therefore, it is crucial to develop a fast and accurate diagnostic system for the timely identification of COVID-19 infected patients and thus to help control its spread.

Methods

This work proposes a new deep CNN based technique for COVID-19 classification in X-ray images. In this regard, two novel custom CNN architectures, namely COVID-RENet-1 and COVID-RENet-2, are developed for COVID-19 specific pneumonia analysis. The proposed technique systematically employs Region and Edge-based operations along with convolution operations. The advantage of the proposed idea is validated by performing series of experimentation and comparing results with two baseline CNNs that exploited either a single type of pooling operation or strided convolution down the architecture. Additionally, the discrimination capacity of the proposed technique is assessed by benchmarking it against the state-of-the-art CNNs on radiologist's authenticated chest X-ray dataset. Implementation is available at https://github.com/PRLAB21/Coronavirus-Disease-Analysis-using-Chest-X-Ray-Images.

Results

The proposed classification technique shows good generalization as compared to existing CNNs by achieving promising MCC (0.96), F-score (0.98) and Accuracy (98%). This suggests that the idea of synergistically using Region and Edge-based operations aid in better exploiting the region homogeneity, textural variations, and region boundary-related information in an image, which helps to capture the pneumonia specific pattern.

Conclusions

The encouraging results of the proposed classification technique on the test set with high sensitivity (0.98) and precision (0.98) suggest the effectiveness of the proposed technique. Thus, it suggests the potential use of the proposed technique in other X-ray imagery-based infectious disease analysis.

Keywords

Coronavirus
COVID-19
Chest X-ray
Region homogeneity
Edge
Convolutional neural network
Transfer learning

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