Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor Multi-Classification
Yazarlar (2)
Arş. Gör. Muhammed ÇELİK
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Doç. Dr. Özkan İNİK
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Expert Systems with Applications |
| Dergi ISSN | 0957-4174 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | Türkçe |
| Basım Tarihi | 03-2024 |
| Cilt No | 238 |
| DOI Numarası | 10.1016/j.eswa.2023.122159 |
| Makale Linki | http://dx.doi.org/10.1016/j.eswa.2023.122159 |
| Özet |
| Accurate classification of magnetic resonance imaging (MRI) images of brain tumors is crucial for early diagnosis and effective treatment in clinical studies. In these studies, many models supported by artificial intelligence (AI) have been proposed as assistant systems for experts. In particular, state-of-the-art deep learning (DL) models that have proven themselves in different fields have been effectively used in the classification of brain MRI images. However, the low accuracy of multiple classification of these images still leads researchers to conduct different studies in this field. Especially there is a need to develop models that achieve high accuracy on original images, and it is believed that this need can be met not only by DL models but also by classical machine learning (ML) algorithms. However, it is critical to choose the hyperparameters correctly for the hybrid use of ML algorithms with DL models. This study … |
| Anahtar Kelimeler |
| Brain tumors | Deep learning | Machine learning | Hyperparameter optimization | Classification |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 71 |
| Google Scholar | 107 |