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Journals / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2026 / Cilt: 13 Sayı: 3

Deep Learning Based Morphometric Analysis for Alzheimer's Diagnosis

Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz

Selahattin Barış Çelebi(Batman Üniversitesi)0000-0002-6235-9348
Bülent Gürsel Emiroğlu(KIRIKKALE ÜNİVERSİTESİ)0000-0002-1656-6450
Pages
1454–1467
Publication Date
2023-09-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

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Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Tensor tabanlı morfometri · Alzheimer · Derin Öğrenme

Abstract

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.

Özet

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.

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Cite This Article

Çelebi, S. B., Emiroğlu, B. G. (2023). Deep Learning Based Morphometric Analysis for Alzheimer's Diagnosis. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 13 Sayı: 3, 1454–1467. https://doi.org/10.21597/jist.1275669

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