Article: COV19-CNNet and COV19-ResNet: Diagnostic Inference Engines for Early Detection of COVID-19.
2021 , Page(s) 1–11
Abstract: Chest CT is used in the COVID-19 diagnosis process as a significant complement to the reverse transcription polymerase chain reaction (RT-PCR) technique. However, it has several drawbacks, including long disinfection and ventilation times, excessive ... ...
| Abstract | Chest CT is used in the COVID-19 diagnosis process as a significant complement to the reverse transcription polymerase chain reaction (RT-PCR) technique. However, it has several drawbacks, including long disinfection and ventilation times, excessive radiation effects, and high costs. While X-ray radiography is more useful for detecting COVID-19, it is insensitive to the early stages of the disease. We have developed inference engines that will turn X-ray machines into powerful diagnostic tools by using deep learning technology to detect COVID-19. We named these engines COV19-CNNet and COV19-ResNet. The former is based on convolutional neural network architecture; the latter is on residual neural network (ResNet) architecture. This research is a retrospective study. The database consists of 210 COVID-19, 350 viral pneumonia, and 350 normal (healthy) chest X-ray (CXR) images that were created using two different data sources. This study was focused on the problem of multi-class classification (COVID-19, viral pneumonia, and normal), which is a rather difficult task for the diagnosis of COVID-19. The classification accuracy levels for COV19-ResNet and COV19-CNNet were 97.61% and 94.28%, respectively. The inference engines were developed from scratch using new and special deep neural networks without pre-trained models, unlike other studies in the field. These powerful diagnostic engines allow for the early detection of COVID-19 as well as distinguish it from viral pneumonia with similar radiological appearances. Thus, they can help in fast recovery at the early stages, prevent the COVID-19 outbreak from spreading, and contribute to reducing pressure on health-care systems worldwide. |
|---|---|
| Language | English |
| Publishing date | 2021-01-06 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 2486574-6 |
| ISSN | 1866-9964 ; 1866-9956 |
| ISSN (online) | 1866-9964 |
| ISSN | 1866-9956 |
| DOI | 10.1007/s12559-020-09795-5 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
Full text online
More links
Kategorien
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.