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Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz

Year 2023, Volume: 13 Issue: 3, 1454 - 1467, 01.09.2023
https://doi.org/10.21597/jist.1275669

Abstract

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.

References

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Deep Learning Based Morphometric Analysis for Alzheimer's Diagnosis

Year 2023, Volume: 13 Issue: 3, 1454 - 1467, 01.09.2023
https://doi.org/10.21597/jist.1275669

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.

References

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There are 51 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Bilgisayar Mühendisliği / Computer Engineering
Authors

Selahattin Barış Çelebi 0000-0002-6235-9348

Bülent Gürsel Emiroğlu 0000-0002-1656-6450

Early Pub Date August 29, 2023
Publication Date September 1, 2023
Submission Date April 2, 2023
Acceptance Date April 28, 2023
Published in Issue Year 2023 Volume: 13 Issue: 3

Cite

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. https://doi.org/10.21597/jist.1275669
AMA Çelebi SB, Emiroğlu BG. Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz. J. Inst. Sci. and Tech. September 2023;13(3):1454-1467. doi:10.21597/jist.1275669
Chicago Çelebi, Selahattin Barış, and Bülent Gürsel Emiroğlu. “Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz”. Journal of the Institute of Science and Technology 13, no. 3 (September 2023): 1454-67. https://doi.org/10.21597/jist.1275669.
EndNote Çelebi SB, Emiroğlu BG (September 1, 2023) Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz. Journal of the Institute of Science and Technology 13 3 1454–1467.
IEEE S. B. Çelebi and B. G. Emiroğlu, “Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz”, J. Inst. Sci. and Tech., vol. 13, no. 3, pp. 1454–1467, 2023, doi: 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 (September 2023), 1454-1467. https://doi.org/10.21597/jist.1275669.
JAMA Çelebi SB, Emiroğlu BG. Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz. J. Inst. Sci. and Tech. 2023;13:1454–1467.
MLA Çelebi, Selahattin Barış and Bülent Gürsel Emiroğlu. “Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz”. Journal of the Institute of Science and Technology, vol. 13, no. 3, 2023, pp. 1454-67, doi:10.21597/jist.1275669.
Vancouver Çelebi SB, Emiroğlu BG. Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz. J. Inst. Sci. and Tech. 2023;13(3):1454-67.

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