| Yazarlar (3) |
Dr. Öğr. Üyesi Fatih UYSAL
Kafkas Üniversitesi, Türkiye |
|
Fırat Hardalaç
Gazi Üniversitesi, Türkiye |
|
Mustafa Koç
Fırat Üniversitesi, Türkiye |
| Özet |
| When looking at prostate cancer, it is seen that it is one of the very common types of cancer in men. In literature review, it is understood that there are a lot of studies for the treatment and diagnosis of this type of cancer with various image processing methods on prostate images. On prostate biopsy, secondary haemorrhage areas of T2-weighted magnetic resonance (MR) in prostate images can cause false diagnoses. T1-weighted MR prostate images help diagnose these cases. In such cases, in order to prevent misdiagnosis; A new classification procedure for MR prostate images with convolutional neural networks (CNN) was performed. As a result of this process, a new deep learning model based on CNN which can classify T1-weighted and T2-weighted MR prostate images has been developed. |
| Anahtar Kelimeler |
| Convolutional neural networks | Deep learning | Magnetic resonance prostate images | Image classification | Artificial intelligence |
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayımlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Web of Science Kapsamındaki Kongre/Sempozyum |
| Bildiri Dili | İngilizce |
| Kongre Adı | International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2019) |
| Kongre Tarihi | 20-04-2019 / 22-04-2019 |
| Basıldığı Ülke | Türkiye |
| Basıldığı Şehir | Antalya |
| Atıf Sayıları | |
| SCOPUS | 1 |
| Google Scholar | 2 |
