Binary Classification of Alzheimer's Disease Using Siamese Neural Network for Early Stage Diagnosis

Yıl 2025, Cilt: 21 Sayı: 2, 152 - 158, 27.06.2025

Ruken Tekin , Tuğba Özge Onur

https://doi.org/10.18466/cbayarfbe.1529546

Öz

Alzheimer's Disease (AD) is a cognitive disease. In individuals with disease, increased brain cell loss is observed over time. This situation leads to deficiencies in memory and thinking ability over time. As a result, significant impairments occur in individuals’ ability to perform primary function. According to research results, the rate of thos disease doubles every five years among people aged between 65 and 85. The causes of AD are unknown and nowadays not definite cure. Early diagnosis of the disease in clinical cure as it has the potential to slow or stop progression. This study aimed to make a prediction based on Magnetic Resonance (MR) images. Images in the standard Alzheimer dataset obtained from the open access database Kaagle were enhanced by applying Gaussian and Median filters. Siamese Neural Network (SNN) categorizes disease stages by learning the similarity between these images. Two categories of images were used from the dataset: Very Mild Dementia (VMD) and Non-Dementia (ND). According to this proposed study, the training accuracy was %99.62 and the validation accuracy %97.67.

Anahtar Kelimeler

Siamese Neural Network , Machine Learning , Alzheimer Disease , Magnetic Resonance Imaging

Kaynakça

• [1]. M. Vasileios, A. Alexiou, Biomarkers for Alzheimer's Disease Diagnosis, Current Alzheimer Research, vol. 14, no. 11, pp. 1149-1154, 2017.
• [2]. S. Przedborski, M. Vila, V. Jackson-Lewis, Neurodegeneration: what is it and where are we?, The Journal of Clinical Investigation, vol. 111, no. 1, pp. 3-10, 2003
• [3]. Türkiye Alzheimer Derneği Resmi Web Sitesi. (2 January 2024). Alzheimer Hastalığı Nedir?, https://www.alzheimerdernegi.org.tr/.
• [4]. S. Murugan, C. Venkatesan, M. G. Sumithra, X. Gao, B. Elakkiya, M. Akila, M. Subramanian, DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images, IEEE Access, vol. 9, pp. 90319-90329, 2021.
• [5]. S. Basaia, F. Agosta, L. Wagner, E. Canu, G. Magnani, R. Santangelo, M. Filippi, Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks, NeuroImage Clinical, vol. 21, 2019.
• [6]. S. Qiu, P. Joshi, M. I. Miller, C. Xue, X. Zhou, C. Karjadi, G. H. Chang, A. S. Joshi, B. Dwyer, S. Zhu, M. C. Kaku, Y. Zhou, Y. J. Alderazi, A. Swaminathan, S. Kedar, M. Saint-Hilaire, S. H. Auerbach, J. Yuan, E. Sartor, R. Au, V. B. Kolachalama, Development and validation of an interpretable deep learning framework for Alzheimer's disease classification, Brain, vol. 143, pp. 1920-1933, 2020.
• [7]. Z. Wan, Y. Dong, Z. Yu, H. Lv, Z. Lv, Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion, Frontiers in Neuroscience, vol. 15, 2021.
• [8]. S. Pala, Alzheimer hastalığının erken teşhisi için biyobelirteçlere dayalı stratejik yol haritası derleme çeviri çalışması, Tıbbi Politika Yazısı, 2021.
• [9]. Zhang, X. Y., Yang, Z. L., Lu, G. M., Yang, G. F., Zhang, L. J., PET/MR Imaging: New Frontier in Alzheimer's Disease and Other Dementias. Frontiers in Molecular Neuroscience, vol. 10, 2017.
• [10].Barthel H., Schroeter M. L., Hoffmann K. T., Sabri O., PET/MR in dementia and other neurodegenerative diseases, Seminars in Nuclear Medicine, vol. 45, pp. 224–233, 2015
• [11]. B. A. Mohammed, E. M. Senan, T. H. Rassem, N. M. Makbol, A. A. Alanazi, Z. G. Al-Mekhlafi, T. S. Almurayziq, F. A. Ghaleb, Multi-Method Analysis of Medical Records and MRI Images for Early Diagnosis of Dementia and Alzheimer’s Disease Based on Deep Learning and Hybrid Methods, Electronics, vol. 10, no. 22, 2021.
• [12]. D. Lu, K. Popuri, G. W. Ding, R. Balachandar, M. F. Beg, Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images, Scientific Reports, vol. 8, 2017.
• [13]. Y. Gupta, K. H. Lee, K. Y. Choi, J. J. Lee B. Kim, G. Kwon, Early diagnosis of Alzheimer's disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images, PloS One, vol. 14, no. 10, 2019.
• [14]. S. Ahmed, K. Y. Choi, J. J. Lee, B. C. Kim, G. Kwon, K. H. Lee, H. B. Jung, Ensembles of Patch-Based Classifiers for Diagnosis of Alzheimer Diseases, IEEE Access, vol. 7, pp. 73373-73383, 2019.
• [15]. H. Nawaz, M. Maqsood, S. Afzal, F. Aadil, I. Mehmood, S. Rho, A deep feature-based real-time system for Alzheimer disease stage detection, Multimedia Tools and Applications, vol. 80, pp. 1-19, 2021.
• [16]. J. Shi, X. Zheng, Y. Li, Q. Zhang, S. Ying, Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease, IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 1, pp. 173-183, 2018.
• [17]. C. F. Liu, S. Padhy, S. Ramachandran, V. Wang, A. Efimov, A. Bernal, L. Shi, M. Vaillant, J. T. Ratnanather, A. V. Faria, B. Caffo, M. S. Albert, M. I. Miller, Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's Disease and Mild Cognitive Impairment, Magnetic Resonance Imaging, vol. 64, pp. 190-199, 2019.
• [18]. X. Bi, W. Liu, H. Liu, Q. Shang, Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease, Journal of Healthcare Engineering, vol. 165, 2021.
• [19]. T. M. Khan, D. G. Bailey, M. A. U. Khan, Y. Kong, Efficient Hardware Implementation For Fingerprint Image Enhancement Using Anisotropic Gaussian Filter, IEEE Transactions on Image Processing, vol. 26, no. 5, pp. 2116-2126, 2017.
• [20]. Y.Lee, S. A. Kassam, Generalized median filtering and related nonlinear filtering techniques, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 33, no. 3, pp. 672-683, 1985.
• [21]. J. Bromley, J. W. Bentz, L. Bottou, I. Guyon, Y. Lecun, E. Sckinger, R. Shah, “Signature Verification Using A Siamese Time Delay Neural Network, Internatioanal Journal of Pattern Recognition Artificial Intelligence, vol. 7, pp. 669-688, 1993.
• [22]. M. Toğaçar, Z. Cömert, B. Ergen, Siyam Sinir Ağlarını Kullanarak Türk İşaret Dilindeki Rakamların Tanımlanması, Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, vol. 23, no. 68, pp. 349-56, 2021.