Selahattin Barış ÇELEBİ, Bülent Gürsel EMİROĞLU

Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz

Alzheimer, dünyadaki en yaygın bunama türüdür ve şu an için kullanılan tedavi yöntemleri sadece hastalığın ilerleyişini önleme amacına yöneliktir. Beyin dokusu hacmi Alzheimer hastalığı (AD) nedeniyle değişir. Tensör tabanlı morfometri (TBM) yardımıyla, hastalığın beyin dokularında neden olduğu değişiklikler izlenebilir. Bu çalışmada AD hastaları ve Bilişsel Normal(ler) (CN'ler) grubu denekleri arasında ayrım yapmak için etkili bir yöntem geliştirmek amaçlanmıştır. TBM veya küçük yerel hacim farklılıkları, sınıflandırma özelliği olarak benimsenmiştir. AD/CN sınıfına ait 3D TBM morfometrik görüntülerinden hipokampus ve temporal lobu kapsayan 5 piksel aralıklı eksenel beyin görüntü dilimleri 2D olarak kaydedildi. Daha sonra her bir klinik gruptan (AD; CN) elde edilen veri setinin %60'ı eğitim, %20’si validasyon ve %20’si test veri setleri olarak ayrıldı (Eğitim: 480; doğrulama: 120; test: 120). Model validasyon (%92.5) ve test (%89) doğruluk değerleri ile AD/CN tahmini gerçekleştirdi. Sonuçlar, Derin öğrenme ile hipokampus ve temporal lobu kapsayan dilimlerden elde edilen TBM'nin AD'nin tanısında yüksek doğrulukla uygulanabileceğini göstermektedir.

Anahtar Kelimeler:

Tensor tabanlı morfometri, Alzheimer, Derin Öğrenme, Veri artırımı

Deep Learning Based Morphometric Analysis for Alzheimer's Diagnosis

Alzheimer's disease is the most common type of dementia in the world, and the treatment methods currently used are aimed only at preventing the progression of the disease. Brain tissue volume changes due to Alzheimer's disease (AD). With the help of tensor-based morphometry (TBM), changes in brain tissues caused by the disease can be monitored. This study aimed to develop an effective method to differentiate between AD patients and the Cognitive Normal (CN) group subjects. TBM, or small local volume differences, are adopted as classification features. Axial brain image slices with 5-pixel intervals covering the hippocampus and temporal lobe from 3D TBM morphometric images belonging to the AD/CN class were recorded in 2D. Then, 60% of the dataset obtained from each clinical group (AD; CN) was allocated as training, 20% as validation, and 20% as test datasets (training: 480; validation: 120; testing: 120). The model performed AD/CN estimation with validation (92.5%) and testing (89%) accuracy values. The results show that TBM obtained from slices covering the hippocampus and temporal lobe with deep learning can be applied with high accuracy in the diagnosis of AD.

Keywords:

Alzheimer, Data augmentation, Deep Learning, Tensor-based morphometry,

Tam Metin

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Bibtex	@araştırma makalesi { jist1275669, journal = {Journal of the Institute of Science and Technology}, eissn = {2536-4618}, address = {}, publisher = {Iğdır Üniversitesi}, year = {2023}, volume = {13}, number = {3}, pages = {1454 - 1467}, doi = {10.21597/jist.1275669}, title = {Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz}, key = {cite}, author = {Çelebi, Selahattin Barış and Emiroğlu, Bülent Gürsel} }
APA	Çelebi, S. B. & Emiroğlu, B. G. (2023). Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz . Journal of the Institute of Science and Technology , 13 (3) , 1454-1467 . DOI: 10.21597/jist.1275669
MLA	Çelebi, S. B. , Emiroğlu, B. G. "Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz" . Journal of the Institute of Science and Technology 13 (2023 ): 1454-1467 <
Chicago	Çelebi, S. B. , Emiroğlu, B. G. "Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz". Journal of the Institute of Science and Technology 13 (2023 ): 1454-1467
RIS	TY - JOUR T1 - Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz AU - Selahattin BarışÇelebi, Bülent GürselEmiroğlu Y1 - 2023 PY - 2023 N1 - doi: 10.21597/jist.1275669 DO - 10.21597/jist.1275669 T2 - Journal of the Institute of Science and Technology JF - Journal JO - JOR SP - 1454 EP - 1467 VL - 13 IS - 3 SN - -2536-4618 M3 - doi: 10.21597/jist.1275669 UR - Y2 - 2023 ER -
EndNote	%0 Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz %A Selahattin Barış Çelebi , Bülent Gürsel Emiroğlu %T Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz %D 2023 %J Journal of the Institute of Science and Technology %P -2536-4618 %V 13 %N 3 %R doi: 10.21597/jist.1275669 %U 10.21597/jist.1275669
ISNAD	Çelebi, Selahattin Barış , Emiroğlu, Bülent Gürsel . "Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz". Journal of the Institute of Science and Technology 13 / 3 (Eylül 2023): 1454-1467 .
AMA	Çelebi S. B. , Emiroğlu B. G. Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz. Iğdır Üniv. Fen Bil Enst. Der.. 2023; 13(3): 1454-1467.
Vancouver	Çelebi S. B. , Emiroğlu B. G. Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz. Journal of the Institute of Science and Technology. 2023; 13(3): 1454-1467.
IEEE	S. B. Çelebi ve B. G. Emiroğlu , "Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz", , c. 13, sayı. 3, ss. 1454-1467, Eyl. 2023, doi:10.21597/jist.1275669