| Yazarlar |
Doç. Dr. Ali Berkan URAL
Kafkas Üniversitesi, Türkiye |
| Özet |
| This study conducts a successful approach on Computer Aided Diagnosis area with using Image processing methods, optimized version of Artificial Neural Network (ANN) with Levenberg Marquardt (LM) algorithm, Deep Learning models (CNN based AlexNet, ResNet-50 and optimized version of CNN with GA). Totally, 1000 patients with COVID-19 (%50 of male and %50 of female), including %50 of severe and %50 of moderate cases and 100 healthy/normal participants were used and evaluated. According to ROC analysis, the case prediction performance/accuracy was provided from ANNLM as %96, AlexNet as %87, ResNet-50 as %95 and CNNGA as %98.5. The assessment of lung pneumonia in COVID-19 chest CT data was successfully achieved by a product available image processing, ANN and deep learning based approach. With using this, fast and accurate detection and classification stages have been successfully achieved by radiologists and doctors. |
| Anahtar Kelimeler |
| Computer aided detection | Computerized Tomography | Interpretation of the abnormality | Lung lesion detection | Novel COVID-19 pneumonia |
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayımlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum |
| Bildiri Dili | İngilizce |
| Kongre Adı | ELECO 2021 |
| Kongre Tarihi | 25-11-2021 / 27-11-2021 |
| Basıldığı Ülke | Türkiye |
| Basıldığı Şehir | Bursa |
| Atıf Sayıları | |
| Google Scholar | 2 |
