Alzheimer ve Parkinson Hastalıklarının Derin Öğrenme Teknikleri Kullanılarak Sınıflandırılması

Bilgisayar destekli cihazların ve sistemlerin sağlık alanında kullanımı oldukça yaygınlaşmıştır. Bu cihaz ve sistemlerin hastalıkların daha hızlı ve erken teşhisine katkısı yüksekti. Özellikle Manyetik Rezonans Görüntüleme (MRI), Bilgisayarlı Tomografi (BT) gibi görüntüleme cihazları; erken teşhisin önemli olduğu hastalıklar özelinde oldukça büyük bir rol oynamaktadır. Nörolojik hastalıklarda da MR ve BT görüntülerinin derin öğrenme modellerinde girdi görüntüsü olarak kullanımı giderek yaygınlaşmaktadır. Bu çalışmada Kaggle sitesi üzerinden elde edilen Alzheimer ve Parkinson hastalıkları teşhisi için “Alzheimer Parkinson 3 Class Data Set” veri setindeki MRI görüntüleri kullanılmıştır. Bu veri seti içerisinde 2561 Alzheimer, 906 Parkinson ve 3010 adet Kontrol (Normal) olmak üzere üç sınıf bulunmaktadır. Bu çalışmada; Alzheimer, Parkinson ve Normal sınıfları, ResNet-18, VGG-16 ve ConvNext mimarisi ile eğitildiğinde sırasıyla %96,2, %95,4 ve %98,9 doğruluk oranı elde edilmiştir. Bunun yanında; Alzheimer ve Parkinson hastalıkları normal sınıfı üzerinde ikili sınıflandırıcılar ile test edilmiştir. Alzheimer- Normal ve Parkinson – Normal sınıfları için eğitilen modellerden ResNet-18 mimarisi sırası ile %82,0 ve %96,1, VGG-16 mimarisi sırası ile %95,4 ve %89,4, ConvNext mimarisi ise %99,4 ve %99,5 başarı oranlarına ulaşılmıştır.

Anahtar Kelimeler:

Alzheimer, Parkinson, Derin Öğrenme, Nörodejeneratif, Manyetik Rezonans

Classification of Alzheimer's and Parkinson's Diseases Using Deep Learning Techniques

The use of computer-aided devices and systems in the field of health has become quite widespread. These devices and systems contributed to faster and earlier diagnosis of diseases. Especially imaging devices such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) play a major role in diseases where early diagnosis is important. In neurological diseases, the use of MRI and CT images as input images in deep learning models is becoming increasingly common. In this study, MRI images from the "Alzheimer's Parkinson's 3 Class Data Set" dataset obtained from the Kaggle website were used to diagnose Alzheimer's and Parkinson's diseases. There are three classes in this data set: 2561 Alzheimer's, 906 Parkinson's and 3010 Control (Normal). In this study, when Alzheimer's Parkinson's and Normal classes were trained with ResNet-18, VGG-16 and ConvNext architecture, accuracy rates of 96.2%, 95.4% and 98.9% were obtained respectively. In addition, Alzheimer's and Parkinson's diseases were tested with binary classifiers on the normal class. Among the models trained for Alzheimer's - Normal and Parkinson's - Normal classes, ResNet-18 architecture achieved 82.0% and 96.1%, VGG-16 architecture achieved 95.4% and 89.4%, and ConvNext architecture achieved 99.4% and 99.5% success rates, respectively.

Keywords:

Alzheimer, Parkinson, Deep Learning, Neurodegenerative, Magnetic Resonance,

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