A Deep Learning Model Based on Convolutional Neural Networks for Classification of Magnetic Resonance Prostate Images
| dc.contributor.author | Uysal, Fatih | |
| dc.contributor.author | Hardalac, Firat | |
| dc.contributor.author | Koc, Mustafa | |
| dc.date.accessioned | 2026-08-12T16:42:15Z | |
| dc.date.issued | 2020 | |
| dc.department | Fırat Üniversitesi | |
| dc.description | International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME) -- APR 20-22, 2019 -- Antalya, TURKEY | |
| dc.description.abstract | 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. | |
| dc.identifier.doi | 10.1007/978-3-030-36178-5_59 | |
| dc.identifier.endpage | 708 | |
| dc.identifier.isbn | 978-3-030-36178-5 | |
| dc.identifier.isbn | 978-3-030-36177-8 | |
| dc.identifier.issn | 2367-4512 | |
| dc.identifier.orcid | 0000-0002-1731-2647 | |
| dc.identifier.scopus | 2-s2.0-85083456297 | |
| dc.identifier.scopusquality | Q3 | |
| dc.identifier.startpage | 701 | |
| dc.identifier.uri | https://doi.org/10.1007/978-3-030-36178-5_59 | |
| dc.identifier.uri | https://hdl.handle.net/11508/46179 | |
| dc.identifier.volume | 43 | |
| dc.identifier.wos | WOS:000678771000059 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Springer International Publishing Ag | |
| dc.relation.ispartof | Artificial Intelligence and Applied Mathematics in Engineering Problems | |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.snmz | KA_WoS_20260511 | |
| dc.subject | Convolutional neural networks | |
| dc.subject | Deep learning | |
| dc.subject | Magnetic resonance prostate images | |
| dc.subject | Image classification | |
| dc.subject | Artificial intelligence | |
| dc.title | A Deep Learning Model Based on Convolutional Neural Networks for Classification of Magnetic Resonance Prostate Images | |
| dc.type | Conference Object |







