Brain Tumor Classification Using Deep Learning Algorithms; [Derin Ogrenme Algoritmalan ile Beyin Tiimoru Siniflandirma]
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Özet
Brain tumor classification categorizes brain tumors based on their various characteristics. It aids in determining suitable treatment strategies and predicting patient outcomes. Therefore, the objective of this study is to identify the optimal tumor classification model by utilizing various Convolutional Neural Network (CNN)-based deep learning models (including combined models) on a recently published tumor classification dataset. Furthermore, diverse image manipulation and augmentation techniques were applied and their impact on model performance was evaluated. The experimental results prove that the combined deep learning models outperform other approaches in terms of performance in multi-class brain tumor classification.
Anahtar Kelimeler
RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/kostu/10208Resmi atıflarda kullanılan kalıcı adresİndeks kayıtları
Kaydın indeks veri tabanlarındaki sayfası (aktarımda alınan kayıt numarasıyla). Erişim, indeksin kendi koşullarına bağlıdır.Atıflar
- Scopus
- 3
Tam Kayıt
| Alan | Değer | Dil |
|---|---|---|
| dc.contributor.author | Pehlivanoglu, Meltem Kurt | |
| dc.contributor.author | Eker, Ayse Gul | |
| dc.contributor.author | Ince, Ibrahim | |
| dc.contributor.author | Karhan, Deniz | |
| dc.contributor.author | Duru, Nevcihan | |
| dc.date.accessioned | 2026-10-27T20:41:05.637091940Z | |
| dc.date.available | 2026-10-27T20:41:05.637091940Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Brain tumor classification categorizes brain tumors based on their various characteristics. It aids in determining suitable treatment strategies and predicting patient outcomes. Therefore, the objective of this study is to identify the optimal tumor classification model by utilizing various Convolutional Neural Network (CNN)-based deep learning models (including combined models) on a recently published tumor classification dataset. Furthermore, diverse image manipulation and augmentation techniques were applied and their impact on model performance was evaluated. The experimental results prove that the combined deep learning models outperform other approaches in terms of performance in multi-class brain tumor classification. | |
| dc.identifier.doi | 10.1109/ubmk59864.2023.10286764 | |
| dc.identifier.endpage | 392 | |
| dc.identifier.isbn | 979-835034081-5 | |
| dc.identifier.scopus | 2-s2.0-85177560250 | |
| dc.identifier.startpage | 387 | |
| dc.identifier.uri | https://rdlopen.rawdatalibrary.net/r/kostu/10208 | |
| dc.language.iso | tr | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.conference | 8th International Conference on Computer Science and Engineering, UBMK 2023 | |
| dc.relation.ispartof | UBMK 2023 - Proceedings: 8th International Conference on Computer Science and Engineering | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.subject | brain tumor | |
| dc.subject | deep learning | |
| dc.subject | machine learning | |
| dc.subject | tumor classification | |
| dc.subject.other | Classification (of information) | |
| dc.subject.other | Convolutional neural networks | |
| dc.subject.other | Deep learning | |
| dc.subject.other | Learning systems | |
| dc.subject.other | Patient treatment | |
| dc.subject.other | Tumors | |
| dc.subject.other | Brain tumor classifications | |
| dc.subject.other | Brain tumors | |
| dc.subject.other | Classification models | |
| dc.subject.other | Combined modeling | |
| dc.subject.other | Convolutional neural network | |
| dc.subject.other | Learning models | |
| dc.subject.other | Machine-learning | |
| dc.subject.other | Network-based | |
| dc.subject.other | Tumor classification | |
| dc.subject.other | Brain | |
| dc.title | Brain Tumor Classification Using Deep Learning Algorithms; [Derin Ogrenme Algoritmalan ile Beyin Tiimoru Siniflandirma] | |
| dc.type | Conference Object | |
| rdl.citation.scopus | 3 | |
| rdl.conference.date | 13 September 2023 through 15 September 2023 | |
| rdl.conference.place | Burdur | |
| rdl.oa.status | closed | |
| rdl.source.index | SCOPUS |
