Derin öğrenme yöntemleri kullanılarak gradyan tabanlı MR görüntülerinde erken safha Alzheimer hastalığının tespiti

Yıl 2024, Cilt: 13 Sayı: 3, 750 - 759, 15.07.2024

Mehmet Emre Sertkaya , Meryem Durmuş , Burhan Ergen

https://doi.org/10.28948/ngumuh.1390830

Öz

Alzheimer Hastalığı (AD), genellikle yaşlılık döneminde görülen bir demans türüdür. AD, beynin sinir hücrelerine yavaşça zarar veren bir nörodejeneratif hastalıktır. Erken evre AD belirtileri genellikle hafif ve tipik olmayan olduğundan teşhisi zorlaştırır. Ancak erken teşhis, hastalığın ilerlemesini yavaşlatma ve uygun tedavi seçenekleri sunma açısından müdahaleyi mümkün kılar. Derin öğrenme yöntemleri, özellikle Manyetik Rezonans (MR) görüntülerinde ince detayları tespit etmek için kullanılabilir. Bu yöntemler, özellikle Manyetik Rezonans Görüntüleme (MRG) gibi görüntüleme tekniklerinde, çeşitli yapıların konumunu, şeklini ve boyutunu belirlemede gradient tabanlı görüntülerin önemli bir rol oynadığı düşünülmektedir. Bu çalışma, farklı açılardaki ve sabit yoğunluktaki beyin MR görüntülerini eğitmek için Derin Öğrenme modellerine önce Gradient filtresi uygulayarak Alzheimer hastalığının erken tespiti konusundaki modellerin performansını artırmayı amaçlamaktadır. Erken aşama AD'yi temsil eden üç farklı kategoriden oluşan görüntü veri seti, hafif bilişsel bozukluğa sahip bireylerin ve sağlıklı bireylerin görüntülerini içermektedir. Orijinal ve gradient filtrelenmiş görüntü alt kümeleri, derin öğrenme modellerine giriş olarak kullanılmıştır. Çalışma sonuçları, gradient tabanlı görüntülerin derin öğrenme modellerinde orijinal görüntülerden daha iyi bir performans sergilediğini göstermektedir. Densenet201 derin öğrenme modeli %98,63'lük en yüksek doğruluk oranını elde etmiştir.

Anahtar Kelimeler

Alzheimer Hastalığı, Derin Öğrenme Teknikleri, Gradyan Filtre, Manyetik Rezonans Görüntüleri, Hafif Bilişsel Bozukluk

Detection of early stage Alzheimer's disease in gradient-based MR images using deep learning methods

Öz

Alzheimer's Disease (AD), a form of dementia prevalent in older age, is a neurodegenerative condition impacting brain nerve cells. Early-stage AD symptoms are often subtle, complicating timely diagnosis. Early detection allows for intervention, slowing disease progression and facilitating appropriate treatments. Deep learning methods, particularly gradient-based images, prove promising for early Alzheimer's detection in Magnetic Resonance (MR) imaging. Gradient-based images, highlighting details in low-intensity images and enhancing contrast, play a vital role in determining structures' location, shape, and size, notably in techniques like Magnetic Resonance Imaging (MRI). This study aims to boost model performance in early AD detection by applying the Gradient filter before training deep learning models on diverse-angle and constant-density brain MRI images. The dataset comprises three categories representing early-stage AD, including images of Mild cognitive impairment and healthy individuals. Original and gradient-filtered image subsets were inputted into deep learning models. Results indicate superior performance of gradient-based images, with the Densenet201 deep learning model achieving the highest accuracy at 98.63%.

Anahtar Kelimeler

Alzheimer, Deep Learning Techniques, Gradient Filter, Magnetic Resonance Images, Mild Cognitive Impairment

Kaynakça

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