Szczegóły publikacji
Opis bibliograficzny
Role of hybrid deep neural networks (HDNNs), computed tomography, and chest X-Rays for the detection of COVID-19 / Muhammad Irfan, Muhammad Aksam Iftikhar, Sana Yasin, Umar Draz, Tariq Ali, Shafiq Hussain, Sarah Bukhari, Abdullah Saeed Alwadie, Saifur Rahman, Adam GŁOWACZ, Faisal Althobiani // International Journal of Environmental Research and Public Health [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1660-4601. — 2021 — vol. 18 iss. 6 art. no. 3056, s. 1-14. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 12-14, Abstr. — Publikacja dostępna online od: 2021-03-16
Autorzy (11)
- Irfan Muhammad
- Iftikhar Muhammad Aksam
- Yasin Sana
- Draz Umar
- Ali Tariq
- Hussain Shafiq
- Bukhari Sarah
- Alwadie Abdullah S.
- Rahman Saifur
- AGHGłowacz Adam
- Althobiani Faisal
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 133118 |
|---|---|
| Data dodania do BaDAP | 2021-03-16 |
| Tekst źródłowy | URL |
| DOI | 10.3390/ijerph18063056 |
| Rok publikacji | 2021 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | International Journal of Environmental Research and Public Health |
Abstract
COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) and X-ray imaging, to predict the risk of the onset of disease in patients suffering from COVID-19. To be precise, the subjects were classified into 3 categories namely normal, Pneumonia, and COVID-19. Initially, the CT and chest X-ray images, denoted as ‘hybrid images’ (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. The 80% hybrid images were used to train the hybrid deep neural network model and the remaining 20% were used for the testing purpose. The capability and prediction accuracy of the HDNNs were calculated using the confusion matrix. The hybrid deep neural network showed a 99% classification accuracy on the test set data.