COVID-19 detection using chest X-ray images based on a developed deep neural network

Abstract

Aim: Currently, a new coronavirus called COVID-19 is the biggest challenge of the human at 21st century. Now, the spread of this virus is such that mortality has risen strongly in all cities of countries. Therefore, it is necessary to think of a solution to handle the disease by fast and timely diagnosis. This paper proposes a method that uses chest X-ray imagery to divide 2-4 classes into 7 different Scenarios, including Bacterial, Viral, Healthy, and COVID-19 classes. The aim of this study is to propose a method that uses chest X-ray imagery to divide 2-4 classes into 7 different Scenarios, including Bacterial, Viral, Healthy, and COVID-19 classes.

Methods: 6 different databases from chest X-ray imagery that have been widely used in recent studies have been gathered for this aim. A Convolutional Neural Network-Long Short Time Memory model is designed and developed to extract features from raw data hierarchically. In order to make more realistic assumptions and use the Proposed Method in the practical field, white Gaussian noise is added to the raw chest X-ray imagery. Additionally, the proposed network is tested and investigated not only on 6 expressed databases but also on two additional databases.

Results: On the test set, the proposed network achieved an accuracy of more than 90% for all Scenarios excluding Scenario V, i.e. Healthy against the COVID-19 against the Viral, and also achieved 99% accuracy for separating the COVID-19 from the Healthy group. The results showed that the proposed network is robust to noise up to 1 dB. It is worth noting that the proposed network for two additional databases, which were only used as test databases, also achieved more than 90% accuracy. In addition, in comparison to the state-of-the-art pneumonia detection approaches, the final results obtained from the proposed network is so promising.

Conclusions: The proposed network is effective in detecting COVID-19 and other lung infectious diseases using chest X-ray imagery and can thus assist radiologists in making rapid and accurate detections.

Fig. 1

The X-ray images of Chest for four groups (from left to right) including healthy, Bacterial, Viral, and COVID19, respectively.

Fig. 2

The block-diagram of the P-M for automatic detection of pneumonia (Bacterial, COVID-19, Viral and Healthy).

Fig. 3

The proposed network architecture for automatic detection of pneumonia based on CNN-LSTM model.

Fig. 4

The proposed CNN-LSTM model error and classification accuracy (based on validation data) for Scenarios I and VII, as well as the confusion matrix, t-SNE charts, and bar chart diagram of precision, sensitivity, accuracy, and specificity (based on testing data) for all Scenarios (I-VII); (a) Error and accuracy, (b) Confusion matrix, (c) t-SNE charts, (d) Bar chart diagram.

Fig. 4

The proposed CNN-LSTM model error and classification accuracy (based on validation data) for Scenarios I and VII, as well as the confusion matrix, t-SNE charts, and bar chart diagram of precision, sensitivity, accuracy, and specificity (based on testing data) for all Scenarios (I-VII); (a) Error and accuracy, (b) Confusion matrix, (c) t-SNE charts, (d) Bar chart diagram.

Fig. 4

The proposed CNN-LSTM model error and classification accuracy (based on validation data) for Scenarios I and VII, as well as the confusion matrix, t-SNE charts, and bar chart diagram of precision, sensitivity, accuracy, and specificity (based on testing data) for all Scenarios (I-VII); (a) Error and accuracy, (b) Confusion matrix, (c) t-SNE charts, (d) Bar chart diagram.

Fig. 5

The performance of the proposed model in comparison to DTL networks, as well as the bar chart diagram of the sensitivity, specificity, accuracy, and precision of each DTL network; (a) Accuracy of the proposed network comparing with DTL networks, (b) Bar chart diagrams.

Fig. 6

The chest X-ray imagery along with white Gaussian noise in the various ranges of SNR and also the classification accuracy of each network (P-M, ResNet 50, VGG 19, Inception, and Xception) for every SNR; (a) Chest X-ray imagery along with white Gaussian noise, (b) The classification accuracy of each network.

Fig. 7

The t-SNE chart, confusion matrix and and the bar chart diagram of sensitivity, specificity, accuracy, and precision of the proposed network for Datasets 1 and 2; (a) The t-SNE chart and confusion matrix for Datasets 1, (b) The t-SNE chart and confusion matrix for Datasets 1, (c) The bar chart diagram.