| Yazarlar |
Ahmed B. Salem Salamh
|
|
Abdulrauf A. Salamah
|
Doç. Dr. Halil İbrahim AKYÜZ
Kastamonu Üniversitesi, Türkiye |
| Özet |
| The chest Computer Tomography (CT scan) is used in the diagnosis of coronavirus disease 2019 (COVID-19) and is an important complement to the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. The paper aims to improve the radiological diagnosis in the case of coronavirus disease COVID-19 pneumonia on forms of noninvasive approaches with conventional and high-resolution computer tomography (HRCT) scan images upon chest CT images of patients confirmed with mild to severe findings. The preliminary study is to compare the radiological findings of COVID-19 pneumonia in conventional chest CT images with images processed by a new tool and reviewed by expert radiologists. The researchers used a new filter called Golden Key Tool (GK-Tool) which has confirmed the improvement in the quality and diagnostic efficacy of images acquired using our modified images. Further, Convolution Neural Networks (CNNs) architecture called VGG face was used to classify chest CT images. The classification has been performed by using VGG face on various datasets which are considered as a protocol to diagnose COVID-19, Non-COVID-19 (other lung diseases), and normal cases (no findings on chest CT). Accordingly, the performance evaluation of the GK-Tool was fairly good as shown in the first set of results, where 80-95% of participants show a good to excellent assessment of the new images view in the case of COVID-19 patients. The results, in general, illustrate good recognition rates in the diagnosis and, therefore, would be significantly higher in normal cases with COVID-19. These results could reduce the radiologist's workload burden and play a major role in the decision-making process. |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | ESCI dergilerinde yayımlanan tam makale |
| Dergi Adı | Radiology Research and Practice |
| Dergi ISSN | 2090-195X |
| Dergi Tarandığı Indeksler | Emerging Sources Citation Index |
| Makale Dili | Türkçe |
| Basım Tarihi | 01-2021 |
| Cilt No | 2021 |
| Sayfalar | 1 / 9 |
| Doi Numarası | 10.1155/2021/5554408 |
| Makale Linki | http://dx.doi.org/10.1155/2021/5554408 |
| Atıf Sayıları | |
| WoS | 6 |
