ADNet: A CNN MODEL FOR ALZHEIMER'S DISEASE DIAGNOSIS ON OASIS-1 DATASET

Ahmet Samed Saraçoğlu; Ayse Merve Acılar; Özlem Erdaş Çiçek

doi:10.17780/ksujes.1534327

TR EN

ADNet: OASIS-1 VERİ KÜMESİ ÜZERİNDE ALZHEİMER HASTALIĞI TEŞHİSİ İÇİN BİR CNN MODELİ

Öz

Alzheimer hastalığı (AD), hafıza, düşünme ve davranış üzerinde ciddi etkileri olan kronik bir nörodejeneratif hastalıktır. Evrişimli Sinir Ağları (CNN) gibi derin öğrenme modelleri, beyin manyetik rezonans görüntüleri (MRI) kullanılarak AD'nin erken aşamalarda tespit edilmesinde umut verici sonuçlar göstermektedir. Bu çalışmada, Alzheimer teşhisi için OASIS-1 veri seti kullanılarak eğitilen ADNet adlı bir CNN modeli önerilmiştir. ADNet'in performansını değerlendirmek için, ilk olarak, her bireyin MR görüntüsünden alınan sagittal düzlemdeki 128 dilimin ilk çeyreğinden, ortasından ve üçüncü çeyreğinden alınan dilimler kullanılarak üç farklı veri seti hazırlanmış ve en bilgilendirici dilim hangisi araştırılmıştır. Her veri seti %80 eğitim ve %20 test olarak ayrılmış ve ilk çeyrek dilimin en iyi performansı gösterdiği saptanmıştır. Ek olarak, elde edilen modelin transfer öğrenme modeli olarak kullanılıp kullanılamayacağı incelenmiştir. Bunun için düşük performanslı bir model, ADNet transfer öğrenme modeli kullanılarak yeniden eğitilmiş ve sonuçların oldukça iyileştiği gözlemlenmiştir. Son olarak, modelin dayanıklılığı 5 katlı çapraz doğrulama ile üç kez tekrarlanarak daha ayrıntılı bir değerlendirmeye tabi tutulmuş ve %95,36 ortalama doğruluk elde edilmiştir. Sonuç olarak, ADNet’in klinik ortamlarda Alzheimer taramasında kullanılabileceği ve hastaların daha erken tedavi almasını sağlayabileceği düşünülmektedir.

Anahtar Kelimeler

ADNet: A CNN MODEL FOR ALZHEIMER'S DISEASE DIAGNOSIS ON OASIS-1 DATASET

Abstract

Alzheimer's disease (AD) is a chronic neurodegenerative disorder affecting memory, thinking, and behavior. Deep learning models, particularly CNNs, have shown promise in detecting AD at initial stages using the brain's magnetic resonance images (MRI). In this study, a CNN model called ADNet, trained using the OASIS-1 dataset, was proposed. The experimental approaches for evaluating the performance of ADNet are as follows: First, three different datasets were prepared using slices taken from the first quarter, middle, and third quarter of the sagittal plane from each MRI, to determine the most informative slice among the 128 slices. Each dataset was split into 80% training and 20% testing. It was found that the first quarter slice showed the best performance. The potential use of the obtained model as a transfer learning model was also examined. For this, a low-performance model was retrained using ADNet as a transfer learning model, and significant improvements in the results were observed. At last, the model’s robustness was evaluated in a more detailed evaluation, using 5-fold cross-validation repeated three times, resulting in a mean accuracy of 97.05%. As a result, ADNet can be used for Alzheimer's screening in clinical settings and could enable patients to receive earlier treatment.

Keywords

Destekleyen Kurum

Necmettin Erbakan Üniversitesi BAP Koordinatörlüğü

Proje Numarası

23GAP19015

Teşekkür

Bu çalışma Necmettin Erbakan Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü tarafından maddi olarak desteklenmiştir [Proje no: 23GAP19015].

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Örüntü Tanıma , Derin Öğrenme , Nöral Ağlar

Bölüm

Araştırma Makalesi

Yazarlar

Ahmet Samed Saraçoğlu
0009-0003-7835-4191
Türkiye

Ayse Merve Acılar ^*
0000-0002-0133-2694
Türkiye

Özlem Erdaş Çiçek
0000-0003-4019-7744
Türkiye

Yayımlanma Tarihi

3 Mart 2025

Gönderilme Tarihi

20 Ağustos 2024

Kabul Tarihi

14 Kasım 2024

Yayımlandığı Sayı

Yıl 2025 Cilt: 28 Sayı: 1

DOI

https://doi.org/10.17780/ksujes.1534327

IZ

https://izlik.org/JA36FD89MX

Kaynak Göster

RIS / Bibtex

APA

Saraçoğlu, A. S., Acılar, A. M., & Erdaş Çiçek, Ö. (2025). ADNet: A CNN MODEL FOR ALZHEIMER’S DISEASE DIAGNOSIS ON OASIS-1 DATASET. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 487-504. https://doi.org/10.17780/ksujes.1534327