A Model Suggestion For Alzheimer’s Disease Diagnosis By Using Deep Learning

Abstract

Alzheimer's disease is one of the greatest health problems of our time. Since there is no cure, it is necessary to diagnose the disease in the early stages and to apply preventive treatments. However, early diagnosis of the disease is very difficult, so most people can be diagnosed after significant and irreversible effects occur. Various studies are carried out by researchers around the world for the early diagnosis of the disease. Deep learning has recently gained importance in the early diagnosis of Alzheimer's disease. With the use of models created using deep learning, the success of early diagnosis has reached high levels. In this study, the stages of Alzheimer's disease and the changes that occur were examined. A literature review was conducted for various techniques used in the diagnosis of Alzheimer's and the use of imaging techniques in the early diagnosis of Alzheimer's was investigated. Due to its widespread use, the MRI technique has been emphasized, and mostly studies using MRI have been examined. Concepts used in deep learning are explained, innovations and results are presented. The architectures used in deep learning and the innovations they bring to this field are revealed, and deep learning models that have been created and tested in current studies are examined. The innovations and success rates brought by various studies have been revealed. Efforts have been made to develop a fast, efficient and successful model that provides ease of use. For this, the scheduler structure, MONAI framework, Data loader structure and various techniques are presented with simple use. Also, the model is optimized to run smoothly on Google Colab. In addition, the tools in the FSL library, which are very important in preprocessing, were studied and optimal parameters were found for the "Bias field and Neck Clean Up", "Standard Brain Extraction Using BET2" and "Robust Brain Center Estimation" tools. By using this library, optimal brain images can be obtained for any model. The DenseNet121 model was used as a basis in the model and it was presented in a structure that can be easily changed. The model can directly use 3D MR images, thus preventing the loss of various spatial information.

Keywords

• Alzheimer’s Disease
• Deep Learning
• Image Recognition
• Early Diagnosis
• Artificial Intelligence

Derin Öğrenme ile Alzheimer Hastalığı Teşhisi İçin Model Önerisi

Öz

Alzheimer hastalığı çağın en büyük sağlık problemlerinden biridir. Bir tedavisi bulunmaması nedeniyle hastalığın erken evrelerde teşhis edilmesi ve önleyici tedavilerin uygulanması gerekmektedir. Ancak hastalığın erken teşhisi oldukça zordur, bu nedenle çoğu kişide belirgin ve geri dönüşsüz etkiler oluştuktan sonra teşhis yapılabilmektedir. Hastalığın erken teşhis edilmesi için dünyada araştırmacılar tarafından çeşitli çalışmalar yapılmaktadır. Deep learning, Alzheimer hastalığının erken teşhisinde son zamanlarda oldukça önem kazanmıştır. Deep learning ile oluşturulmuş modellerin kullanılmasıyla erken teşhis yapılabilme başarısı yüksek seviyelere ulaşmıştır. Bu çalışmada Alzheimer hastalığının oluşum evreleri ve oluşan değişiklikler incelenmiştir. Alzheimer’s teşhisinde kullanılan çeşitli teknikler için literatür taraması yapılmış ve görüntüleme tekniklerinin Alzheimer’s erken teşhisinde kullanımı araştırılmıştır. Yaygın kullanımı nedeniyle MRI tekniği üzerinde durulmuş, çoğunlukla MRI kullanılan çalışmalar incelenmiştir. Deep learning’te kullanılan kavramlar açıklanmış, yenilikler ve sonuçlar ortaya konmuştur. Deep learning’te kullanılan mimariler ve bu alanda getirdikleri yenilikler ortaya konmuş, mevcut çalışmalarda oluşturulmuş ve test edilmiş deep learning modelleri incelenmiştir. Yapılan çeşitli çalışmaların getirdiği yenilikler ve başarı oranları ortaya konmuştur. Kullanım kolaylığı sağlayan ve hızlı, performanslı ve başaırılı bir model geliştirilmesi için çalışılmıştır. Bunun için scheduler yapısı, MONAI yapısı, “Data loader” yapısı ve çeşitli teknikler basit bir kullanımla sunulmuştur. Ayrıca model Google Colab üzerinde sorunsuz şekilde çalışması için optimize edilmiştir. Ayrıca görüntü önişlemede oldukça önemli olan FSL kütüphanesindeki toollar ile çalışılmış ve "Bias field and Neck Clean Up", “Standard Brain Extraction Using BET2” ve "Robust Brain Center Estimation" toolları için optimal parametreler bulunmuştur. Bu kütüphane ile herhangi bir model için optimal beyin görüntüleri elde edilebilmektedir. Modelde temel olarak DenseNet121 modeli kullanılmıştır ve kolaylıkla model değiştirilebilen bir yapıda sunulmuştur. Model 3 boyutlu MR görüntülerini doğrudan kullanabilmektedir ve bu sayede çeşitli uzaysal bilginin kaybının önüne geçilmiştir.

Anahtar Kelimeler

• Alzheimer Hastalığı
• Derin Öğrenme
• Görüntü Tanıma
• Erken Teşhis
• Yapay Zeka

Kaynakça

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