Deep learning based COVID-19 detection via hard voting ensemble method
| dc.contributor.author | Shareef, Asaad Qasim | |
| dc.contributor.author | Kurnaz, Sefer | |
| dc.date.accessioned | 2023-06-08T06:16:36Z | |
| dc.date.available | 2023-06-08T06:16:36Z | |
| dc.date.issued | 2023 | en_US |
| dc.department | Enstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı | en_US |
| dc.description.abstract | Healthcare systems throughout the world are under a great deal of strain because to the continuing COVID-19 epidemic, making early and precise diagnosis critical for limiting the virus’s propagation and efficiently treating victims. The utilization of medical imaging methods like X-rays can help to speed up the diagnosis procedure. Which can offer valuable insights into the virus’s existence in the lungs. We present a unique ensemble approach to identify COVID-19 using X-ray pictures (X-ray-PIC) in this paper. The suggested approach, based on hard voting, combines the confidence scores of three classic deep learning models: CNN, VGG16, and DenseNet. We also apply transfer learning to enhance performance on small medical image datasets. Experiments indicate that the suggested strategy outperforms current techniques with a 97% accuracy, a 96% precision, a 100% recall, and a 98% F1-score.These results demonstrate the effectiveness of using ensemble approaches and COVID-19 transfer-learning diagnosis using X-ray-PIC, which could greatly aid in early detection and reducing the burden on global health systems. | en_US |
| dc.identifier.citation | Shareef, A. Q., Kurnaz, S. (2023). Deep learning based COVID-19 detection via hard voting ensemble method. Wireless Personal Communications. | en_US |
| dc.identifier.issn | 0929-6212 | |
| dc.identifier.scopus | 2-s2.0-85159085442 | |
| dc.identifier.scopusquality | Q2 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/3507 | |
| dc.identifier.wos | WOS:000984210700001 | |
| dc.identifier.wosquality | Q3 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.indekslendigikaynak | PubMed | |
| dc.institutionauthor | Shareef, Asaad Qasim | |
| dc.institutionauthor | Kurnaz, Sefer | |
| dc.language.iso | en | |
| dc.publisher | Springer | en_US |
| dc.relation.ispartof | Wireless Personal Communications | |
| dc.relation.isversionof | 10.1007/s11277-023-10485-2 | en_US |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - İdari Personel ve Öğrenci | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | CNN | en_US |
| dc.subject | COVID-19 | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Ensemble method | en_US |
| dc.subject | Hard voting | en_US |
| dc.title | Deep learning based COVID-19 detection via hard voting ensemble method | |
| dc.type | Article |
