Derin Sinir Ağı Mimarileri ile Alzheimer Hastalığı MR Görüntülerinin Evre Sınıflandırılması

Yıl 2026, Cilt: 8 Sayı: 1 , 93 - 103 , 30.04.2026

Aslıhan Güven , Abdurrahim Hüseyin Ezirmik , Mustafa Furkan Ceylan , Fatih Aydın

https://doi.org/10.46387/bjesr.1869200

https://izlik.org/JA34WN94PR

Öz

Bu çalışma, yapısal manyetik rezonans görüntüleme (MRI) verileri kullanılarak Alzheimer hastalığının evre sınıflandırması için beş derin öğrenme mimarisini karşılaştırmaktadır. Deneylerde, gerçek hayattaki dört farklı klinik Alzheimer evrelerinden elde edilmiş beyin MRI görüntülerini içeren OASIS veri seti kullanılmıştır. Üç boyutlu MR görüntüleri iki boyutlu koronal dilimlere dönüştürülmüştür. Karşılaştırmada tutarlılığı sağlamak amacıyla tüm modeller aynı parametrelerle eğitilmiştir. Performans değerlendirmesi, test kümesi üzerinde doğruluk, F1 skoru ve makro ortalamalı ROC AUC ölçütleri kullanılarak yapılmıştır. Ayrıca eğitim süresi ve model boyutu gibi hesaplama ile ilgili faktörler de dikkate alınmıştır. Sonuçlar, InceptionV3 modelinin hastalık evreleri genelinde en güvenilir performansı sunduğunu göstermektedir. MobileNetV2 ise benzer test doğruluğu elde ederken çok daha düşük hesaplama kaynağı gerektirmesi sayesinde kaynak kısıtlı ortamlarda dağıtım için pratik bir seçenek olarak öne çıkmaktadır.

Anahtar Kelimeler

Derin Öğrenme , Transfer Öğrenme , Alzheimer Hastalığı , MR , Model Karşılaştırması

Stage Classification of Alzheimer’s Disease MRI Data via Deep Neural Network Architectures

https://izlik.org/JA34WN94PR

Öz

This study compares five deep learning architectures for Alzheimer's disease stage classification using structural magnetic resonance imaging (MRI) data. The experiments are conducted using the OASIS dataset, which comprises brain MRI scans representing multiple clinical stages of Alzheimer’s disease obtained from real-world clinical scenarios. In the preprocessing stage, three-dimensional MRI volumes are transformed into two-dimensional coronal slices for subsequent analysis. Deep learning models are trained and assessed using the same parameters to maintain consistency in comparison. Accuracy, F1 score, and macro-averaged ROC AUC are used to measure performance on the test set. Additionally, computational aspects such as training time and model complexity are taken into consideration. The results show that InceptionV3 delivers the most reliable overall performance across disease stages. MobileNetV2 performs similar test accuracy while requiring much lower computational cost, and it is a practical choice for deployment in resource constrained environments.

Anahtar Kelimeler

Deep Learning , Transfer Learning , Alzheimer’s Disease , MRI , Model Benchmarking

Kaynakça

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