| Yazarlar (3) |
Doç. Dr. Ali Berkan URAL
Türkiye |
Pınar Özışık
Türkiye |
|
Fırat Hardalaç
Gazi Üniversitesi, Türkiye |
| Özet |
| Detection of masses can be a challenging task for radiologists and physicians. Manual tumor diagnosis in the brain is sometimes a time consuming process and can be insufficient for fast and accurate detection and interpretation. This study introduces an improved interface-supported early diagnosis system to increase the speed and accuracy for supporting the traditional methods. The first stage in the system involves collecting information from the brain tissue, and assessing whether it is normal or abnormal through the processing of Magnetic Resonance Imaging (MRI) and Computerized Tomography (CT) images. The next stage involves gathering results from the image(s) after the single/multiple and volumetric and multiscale image analysis. The other stage involves Feature Extraction for some cases and making an interpretation about the abnormal Region of Interest (ROI) area via Deep Learning and … |
| Anahtar Kelimeler |
| Computer-aided medical diagnosis systems | Abnormality detection and localization | Classification of brain masses | Deep learning | Artificial intelligence |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | MULTIMEDIA TOOLS AND APPLICATIONS |
| Dergi ISSN | 1380-7501 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 06-2020 |
| Cilt No | 79 |
| Sayı | 21 |
| Sayfalar | 15613 / 15634 |
| Doi Numarası | 10.1007/s11042-019-07823-7 |
| Makale Linki | https://link.springer.com/article/10.1007/s11042-019-07823-7 |
| Atıf Sayıları | |
| WoS | 5 |
| Google Scholar | 12 |
