Shareef, Asaad QasimKurnaz, Sefer2023-06-082023-06-082023Shareef, A. Q., Kurnaz, S. (2023). Deep learning based COVID-19 detection via hard voting ensemble method. Wireless Personal Communications.0929-6212https://hdl.handle.net/20.500.12939/3507Healthcare 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.eninfo:eu-repo/semantics/openAccessCNNCOVID-19Deep learningEnsemble methodHard votingArticle2-s2.0-85159085442Q2WOS:000984210700001Q3