TY  - JOUR
T1  - Multi-Class Brain Tumor MRI Classification Using MS-GHOST-RAAE on a Combined Figshare-Br35H-SARTAJ Dataset
TT  - Figshare-Br35H-SARTAJ Veri Kümesinin Birleştirilmesiyle MS-GHOST-RAAE Kullanılarak Çok Sınıflı Beyin Tümörü MR Sınıflandırması
AU  - Suiçmez, Çağrı
AU  - Yılmaz, Cemal
AU  - Suiçmez, Alihan
AU  - Işık, Mehmet Fatih
PY  - 2026
DA  - June
Y2  - 2026
DO  - 10.29002/asujse.1950042
JF  - Aksaray University Journal of Science and Engineering
JO  - Aksaray J. Sci. Eng.
PB  - Aksaray University
WT  - DergiPark
SN  - 2587-1277
SP  - 32
EP  - 58
VL  - 10
IS  - 1
LA  - en
AB  - This study proposes a hybrid deep learning framework for multi-class brain tumor classification using a combined MRI dataset constructed from Figshare, Br35H, and SARTAJ sources. The dataset includes 7023 MRI images belonging to four clinically important classes: glioma, meningioma, no-tumor, and pituitary tumor. In the proposed approach, a dedicated preprocessing pipeline is first applied to enhance tumor-related image regions and reduce irrelevant background information. Then, the Multi-scale GHOST Residual Attention Autoencoder (MS-GHOST-RAAE) is used for deep feature extraction. This architecture integrates multi-scale GHOST modules, residual connections, and attention mechanisms to obtain compact, stable, and discriminative feature representations. The extracted features are subsequently classified using conventional machine learning classifiers. Experimental results show that the proposed hybrid framework achieves 98.75% accuracy on the combined dataset, with a total processing time of 543 + 269.4571 s. These findings indicate that MS-GHOST-RAAE provides strong classification performance on heterogeneous MRI images and offers an effective computer-aided decision-support approach for brain tumor classification.
KW  - Brain tumor classification
KW  - Deep learning
KW  - MS-GHOST-RAAE
KW  - Machine learning
KW  - Wavelet transformation
KW  - MRI
N2  - Bu çalışma, Figshare, Br35H ve SARTAJ kaynaklarından oluşturulan birleşik bir MRI veri kümesi kullanılarak çok sınıflı beyin tümörü sınıflandırması için hibrit bir derin öğrenme çerçevesi önermektedir. Veri kümesi, klinik olarak önemli dört sınıfa ait 7023 MRI görüntüsünü içermektedir: gliom, menenjiom, tümörsüz ve hipofiz tümörü. Önerilen yaklaşımda, öncelikle tümörle ilgili görüntü bölgelerini iyileştirmek ve ilgisiz arka plan bilgilerini azaltmak için özel bir ön işleme hattı uygulanmaktadır. Daha sonra, derin özellik çıkarımı için Çok Ölçekli GHOST Artık Dikkat Otomatik Kodlayıcı (MS-GHOST-RAAE) kullanılmaktadır. Bu mimari, kompakt, kararlı ve ayırt edici özellik temsilleri elde etmek için çok ölçekli GHOST modüllerini, artık bağlantıları ve dikkat mekanizmalarını entegre eder. Çıkarılan özellikler daha sonra geleneksel makine öğrenmesi sınıflandırıcıları kullanılarak sınıflandırılır. Deneysel sonuçlar, önerilen hibrit çerçevenin birleşik veri kümesinde %98,75 doğruluk oranına ulaştığını ve toplam işlem süresinin 543 + 269,4571 s olduğunu göstermektedir. Bu bulgular, MS-GHOST-RAAE'nin heterojen MRI görüntülerinde güçlü sınıflandırma performansı sağladığını ve beyin tümörü sınıflandırması için etkili bir bilgisayar destekli karar verme yaklaşımı sunduğunu göstermektedir.
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UR  - https://doi.org/10.29002/asujse.1950042
L1  - https://dergipark.org.tr/en/download/article-file/6007792
ER  -