Automated COVID-19 Detection from Chest X-Ray Images: A High-Resolution Network (HRNet) Approach

Abstract

The pandemic, originated by novel coronavirus 2019 (COVID-19), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 in the human body to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the non-invasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. Our intensive background studies show that, the works till now are not efficient to produce an unbiased detection result. In this work, we proposed an automated COVID-19 classification method, utilizing available COVID and non-COVID X-Ray datasets, along with High-Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes. To evaluate the proposed method, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got a significant result of 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models. Finally, we conclude that our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease.

Keywords: COVID-19; HRNet; Healthcare; Pandemic; UNet; X-Ray.

Fig. 1

Proposed model to classify COVID-19 disease using HRNet

Fig. 2

A class distribution representation of the public repository

Fig. 3

Variations observed in COVID dataset [a side view, b washed out, c color issue, d and e markers, f and g medical equipment]

Fig. 4

Image comparison of COVID and non-COVID dataset [a COVID dataset, b NIH dataset, and c ChexPert dataset]

Fig. 5

Image comparison used in lung segmentation [a COVID dataset, b Montgomery dataset, and c Shenzen dataset]

Fig. 6

Segmented region after dilation and closing apply UNet architecture

Fig. 7

Sample images after augmentation

Fig. 8

A general architecture of HRNet for feature extraction

Fig. 9

This figure visualizes some sample heatmaps (blue shade represents the focus region) of corona positive detection by the classifier. But as we can see in the figure, most of the focused regions redundant, not significant, erroneous and somewhat can produce a biased result in times of classification

Fig. 10

Training and validation curves after training the UNet architecture

Fig. 11

Classification Head

Fig. 12

Confusion matrix after training the proposed model

Fig. 13

Heatmaps in a visualize some sample for positive detection and in b represent corona negative detection. As we can see, the lung regions are accurately focused (blue shade represents the focus region) by the classifier after training with segmented data

Fig. 14

Confusion matrix after training the existing models