Efficient artificial intelligence-based models for COVID-19 disease detection and diagnosis from CT-Scans
| dc.authorid | Alaa Ali Hameed / 0000-0002-8514-9255 | en_US |
| dc.authorscopusid | Alaa Ali Hameed / 56338374100 | en_US |
| dc.authorwosid | Alaa Ali Hameed / ABI-8417-2020 | |
| dc.contributor.author | Masood, Muhammad Zargham | |
| dc.contributor.author | Jamil, Akhtar | |
| dc.contributor.author | Hameed, Alaa Ali | |
| dc.date.accessioned | 2022-11-07T07:22:59Z | |
| dc.date.available | 2022-11-07T07:22:59Z | |
| dc.date.issued | 2022 | en_US |
| dc.department | İstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
| dc.description.abstract | COVID-19 is contagious virus that first emerged in China in 2019's last month. It mainly infects the both the lungs and the respiratory system. The virus has severely impacted life and the economy, which exposed threats to governments worldwide to manage it. Early diagnosis of COVID-19 could help with treatment planning and disease prevention strategies. In this study, we use CT-Scanned images of the lungs to show how COVID-19 may be identified using transfer learning model and investigate which model achieved the best and fastest results. Our primary focus was to detect structural anomalies to distinguish among COVID-19 positive, negative, and normal cases with deep learning methods. Every model received training with and without transfer learning and results were compared for various versions of DenseNet and EfficientNet. Optimal results were obtained using DenseNet201 (99.75%). When transfer learning was applied, all models produced almost similar results. | en_US |
| dc.identifier.citation | Masood, M. Z., Jamil, A., & Hameed, A. A. (2022). Efficient artificial intelligence-based models for COVID-19 disease detection and diagnosis from CT-scans. Paper presented at the 2022 2nd International Conference on Computing and Machine Intelligence, ICMI 2022 - Proceedings, doi:10.1109/ICMI55296.2022.9873659 | en_US |
| dc.identifier.doi | 10.1109/ICMI55296.2022.9873659 | en_US |
| dc.identifier.scopus | 2-s2.0-85138990130 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://doi.org/10.1109/ICMI55296.2022.9873659 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12713/3241 | |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.institutionauthor | Hameed, Alaa Ali | |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | 2022 2nd International Conference on Computing and Machine Intelligence, ICMI 2022 - Proceedings | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Convolutional Neural Networks (CNNs) | en_US |
| dc.subject | COVID-19 | en_US |
| dc.subject | Deep Learning Models | en_US |
| dc.subject | Transfer Learning | en_US |
| dc.subject | CT Scans | en_US |
| dc.title | Efficient artificial intelligence-based models for COVID-19 disease detection and diagnosis from CT-Scans | en_US |
| dc.type | Conference Object | en_US |
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