Detection of Alzheimer’s Disease with Deep Learning Method

Öz

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.

Anahtar Kelimeler

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

Derin Öğrenme Yöntemiyle Alzheimer Hastalığının Tespiti

Öz

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.

Anahtar Kelimeler

• Alzheimer hastalığı tespiti
• Derin öğrenme
• CNN
• SVM

Kaynakça

1. Referans1 Alzheimer's Disease International & McGill University. World Alzheimer Report 2021, 2021.
2. Referans2 Stelzmann R., Schnitzlein HN, Murtagh FR. An English Translation of Alzheimer’s 1907 Paper, “Über eine eigenartige Erkankung der Hirnrinde”. Clinical Anatomy, 1995; 8(6): 429-431.
3. Referans3 Small GW, Rabins PV, Barry PP, et al. Diagnosis and treatment of Alzheimer disease and related disorders: consensus statement of the American Association for Geriatric Psychiatry, the Alzheimer’s Association, and the American Geriatrics Society. The Journal of the American Medical Association, 1997; 278(16): 1363-1371.
4. Referans4 Alzheimer Hastalığı Nedir?. https://www.alzheimerdernegi.org.tr/alzheimer-hastaligi-nedir/. Erişim tarihi: Nisan 2022
5. Referans5 Liu S, Liu S, Cai W. Early Diagnosis of Alzheimer’s Disease with Deep Learning, IEEE, 2014.
6. Referans6 Islam J, Zhang Y. A Novel Deep Learning Based Multi-class Classification Method for Alzheimer’s Disease Detection Using Brain MRI Data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017; 10654: 213-222.
7. Referans7 Ramzan F, Khan MUG, Rehmat A, et al. A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks. Journal of Medical Systems, 2020; 44(2): 1-16.
8. Referans8 Subramoniam M, Aparna TR, Anurenjan PR, Sreeni KG. Deep Learning-Based Prediction of Alzheimer’s Disease from Magnetic Resonance Images. Intelligent Vision in Healthcare, 2022: 145-151.

9. Referans9 Suzuki K. Artificial Neural Networks: Methodological Advances and Biomedical Applications. IntechOpen, London, 2011.
10. Referans10 Da Silva IN, Spatti DH, Flauzino RA, Liboni LHB, dos Reis Alves SF. Artificial Neural Networks. Cham: Springer International Publishing, Switzerland, 2017.
11. Referans11 Krogh A. What are artificial neural networks?. Nature Biotechnology, 2008; 26(2): 195-197.
12. Referans12 O’Shea K, Nash R. An Introduction to Convolutional Neural Networks. arXiv Prepr. arXiv151108458. 2015.
13. Referans13 Convolutional Neural Network | Deep Learning | Developers Breach. https://developersbreach.com/convolution-neural-network-deep-learning/. Accessed: May 2022
14. Referans14 Kim P. MATLAB Deep Learning. Apress Berkeley, Califonia, 2017.
15. Referans15 Gu J, Wang Z, Kuen J, et al. Recent advances in convolutional neural networks. Pattern Recognition, 2018; 77: 354-377.
16. Referans16 İnik Ö, Ülker E. Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri. Gaziosmanpasa Journal of Scientific Research, 2017; 6(3): 85-104.
17. Referans17 Guo T, Dong J, Li H, Gao Y. Simple convolutional neural network on image classification. 2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017, 2017: 721-724.