Deep learning based brain tumor classification and detection system
| dc.contributor.author | Arı, Ali | |
| dc.contributor.author | Hanbay, Davut | |
| dc.date.accessioned | 2019-07-08T12:33:28Z | |
| dc.date.available | 2019-07-08T12:33:28Z | |
| dc.date.issued | 2018 | |
| dc.department | İnönü Üniversitesi | en_US |
| dc.description.abstract | The brain cancer treatment process depends on the physician's experience and knowledge. For this reason, using an automated tumor detection system is extremely important to aid radiologists and physicians to detect brain tumors. The proposed method has three stages, which are preprocessing, the extreme learning machine local receptive fields (ELM-LRF) based tumor classification, and image processing based tumor region extraction. At first, nonlocal means and local smoothing methods were used to remove possible noises. In the second stage, cranial magnetic resonance (MR) images were classified as benign or malignant by using ELM-LRF. In the third stage, the tumors were segmented. The purpose of the study was using only cranial MR images, which have a mass, in order to save the physician's time. In the experimental studies the classification accuracy of cranial MR images is 97.18%. Evaluated results showed that the proposed method's performance was better than the other recent studies in the literature. Experimental results also proved that the proposed method is effective and can be used in computer aided brain tumor detection. | en_US |
| dc.identifier.citation | Arı, A. Hanbay, D. (2018). Deep learning based brain tumor classification and detection system. Cilt:26 Sayı:5, 2275-2286 ss. | en_US |
| dc.identifier.doi | 10.3906/elk-1801-8 | en_US |
| dc.identifier.endpage | 2286 | en_US |
| dc.identifier.issn | 1300-0632 | |
| dc.identifier.issn | 1300-0632 | |
| dc.identifier.issue | 5 | en_US |
| dc.identifier.scopus | 2-s2.0-85054551418 | en_US |
| dc.identifier.scopusquality | Q3 | en_US |
| dc.identifier.startpage | 2275 | en_US |
| dc.identifier.trdizinid | 323634 | en_US |
| dc.identifier.uri | https://doi.org/10.3906/elk-1801-8 | |
| dc.identifier.uri | https://hdl.handle.net/11616/12414 | |
| dc.identifier.uri | https://search.trdizin.gov.tr/yayin/detay/323634 | |
| dc.identifier.volume | 26 | en_US |
| dc.identifier.wos | WOS:000448109200009 | en_US |
| dc.identifier.wosquality | Q4 | en_US |
| dc.indekslendigikaynak | TR-Dizin | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, ATATURK BULVARI NO 221, KAVAKLIDERE, ANKARA, 00000, TURKEY | en_US |
| dc.relation.ispartof | Turkısh journal of electrıcal engıneerıng and computer scıences | en_US |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Image Segmentatıon | en_US |
| dc.title | Deep learning based brain tumor classification and detection system | en_US |
| dc.type | Article | en_US |
