Detection of Alzheimer’s Disease with Deep Learning Method

Year 2022, Volume: 34 Issue: 2, 879 - 887, 30.09.2022

Gözde Sena Karabay , Mehmet Çavaş

https://doi.org/10.35234/fumbd.1137246

Cited By: 2

Abstract

Alzheimer's disease is a common type of dementia that is a progressive neurodegenerative disease with no cure. Many imaging techniques are used to diagnose the disease. One of these techniques is Magnetic Resonance Imaging (MRI). Early diagnosis is of great importance for the patient and his family in slowing the progression of the disease and taking the necessary precautions. Supportive studies have been carried out on this subject with deep learning methods for early and accurate diagnosis. At the same time, deep learning methods are used to follow the course of the disease. This study was carried out using AlexNet and MobileNetV2 architectures and CNN (Convolutional Neural Network) model. These features were combined by extracting features from both architectures using a total of 6400 MR images. Feature selection was made using the NCA (Neighbourhood Components Analysis) algorithm and classification were performed with SVM (Support Vector Machine). 100% accuracy value was calculated in the studied model.

Keywords

Alzheimer’s disease detection, Deep learning, CNN, SVM.

Derin Öğrenme Yöntemiyle Alzheimer Hastalığının Tespiti

Abstract

Alzheimer hastalığı yaygın olarak görülen bir demans türü olup, ilerleyen ve tedavisi bulunmayan nörodejeneratif bir hastalıktır. Hastalığı teşhis edebilmek için birçok görüntüleme tekniği kullanılmaktadır. Bu tekniklerden biri Manyetik Rezonans Görüntüleme (MRG) tekniğidir. Erken teşhis edilmesi hastalığın ilerlemesini yavaşlatmak ve gerekli önlemleri alma konusunda hasta ve ailesi için büyük önem taşımaktadır. Erken ve doğru teşhis için derin öğrenme yöntemleriyle bu konuda destekleyici çalışmalar gerçekleştirilmiştir. Aynı zamanda hastalığın seyrini takip etmek için de derin öğrenme yöntemleri kullanılmaktadır. Bu çalışmada hastalığın teşhisi için AlexNet, MobileNetV2 mimarileri ve ESA (Evrişimsel Sinir Ağları) modeli kullanılarak gerçekleştirilmiştir. Toplamda 6400 adet MR görüntüsü kullanılarak her iki mimariden özellik çıkarma işlemi yapılarak bu özellikler birleştirilmiştir. KBA (Komşuluk Bileşen Analizi) algoritması kullanılarak özellik seçimi yapılmış ve DVM (Destek Vektör Makineleri) ile sınıflandırma işlemi gerçekleştirilmiştir. Çalışılan modelde %100 doğruluk değeri hesaplanmıştır.

Keywords

Alzheimer hastalığı tespiti, Derin öğrenme, CNN, SVM

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