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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. |
