Deep Learning Model to Evaluate Alzheimer's disease Through Multi-View Clustering

Sneha Nimbare; Priyanka Paygude; Amol Dhumane; Snehal Rathi; Vijaykumar Bidve

doi:10.54392/irjmt2513

Authors

Sneha Nimbare Bharati Vidyapeeth (Deemed to be University), College of Engineering, Pune, India Author https://orcid.org/0009-0007-1331-6257
Priyanka Paygude Bharati Vidyapeeth (Deemed to be University), College of Engineering, Pune, India Author https://orcid.org/0000-0002-3162-8453
Amol Dhumane Symbiosis Institute of Technology, Symbiosis International University, Lavale, Pune, India Author https://orcid.org/0000-0002-7529-4125
Snehal Rathi BRACT’S Vishwakarma Institute of Information Technology, Pune, India Author https://orcid.org/0009-0004-7638-6429
Vijaykumar Bidve School of CSIT, Symbiosis Skills and Professional University, Kiwale, Pune, India Author https://orcid.org/0000-0001-6490-9262

DOI:

https://doi.org/10.54392/irjmt2513

Keywords:

Alzheimer’s Disease, Channel Boost-Convolution Neural Network, Decision Tree, Deep Learning, Image Classification, Image Clustering, Multi-View Assembling Approach

Abstract

Early diagnosis of Alzheimer's disease (AD) plays a crucial role in the development and effectiveness of interventions, and neuroimaging stands out as an up-and-coming field for the initial identification of Alzheimer's disease. Earlier models utilized various methods to analyze images of Alzheimer's disease, such as deep learning models or unsupervised matrix factorization processes. Neither of these techniques alone can produce satisfactory results while clustering multi-view photos of Alzheimer's disease. This motivates our research to create a deep learning model for obtaining the most important Alzheimer's disease factors from MRI and classifying brain images into different stages. To achieve optimal results in multi-view clustering, the proposed model integrates a deep learning technique (Channel Boost-Convolution Neural Network) with an inverse matrix factorization method, forming an ensemble approach. The experiment analyzes several images to evaluate the implemented technique for the performance of RMSE, which are about 2.32 better than the various compared models. The results show that combining the deep learning model with Inverse matrix factorization for Alzheimer's disease multi-view image clustering works well, the Transformers can further improve multi-view clustering in deep learning.

References

D.A. Arafa, H.E.D. Moustafa, A.M. Ali-Eldin, H.A. Ali, Early detection of Alzheimer’s disease based on the state-of-the-art deep learning approach: a comprehensive survey. Multimedia Tools and Applications, 81(17), (2022) 23735-23776. https://doi.org/10.1007/s11042-022-11925-0

M. Odusami, R. Maskeliūnas, R. Damaševičius, S. Misra, Explainable deep-learning-based diagnosis of Alzheimer’s disease using multimodal input fusion of PET and MRI Images. Journal of Medical and Biological Engineering, 43(3), (2023) 291-302. https://doi.org/10.1007/s40846-023-00801-3

G. Hcini, I. Jdey, H. Dhahri, Investigating Deep Learning for Early Detection and Decision-Making in Alzheimer’s Disease: A Comprehensive Review. Neural Process Lett 56, (2024) 153. https://doi.org/10.1007/s11063-024-11600-5

Eqtidar M. Mohammed, Ahmed M. Fakhrudeen, Omar Younis Alani, Detection of Alzheimer's disease using deep learning models: A systematic literature review. Informatics in Medicine Unlocked, 50, (2024) 101551. https://doi.org/10.1016/j.imu.2024.101551

A. Gamal, M. Elattar, S. Selim, Automatic early diagnosis of Alzheimer’s disease using 3D deep ensemble approach. IEEE Access, 10, (2022) 115974-115987. https://doi.org/10.1109/ACCESS.2022.3218621

M. Kaya Keleş, Ü. Kiliç, Classification of Brain Volumetric Data to Determine Alzheimer’s Disease Using Artificial Bee Colony Algorithm as Feature Selector. IEEE Access, 10, (2022) 82989-83001. https://doi.org/10.1109/ACCESS.2022.3196649

C. M. Chabib, L. J. Hadjileontiadis and A. A. Shehhi, DeepCurvMRI: Deep Convolutional Curvelet Transform-Based MRI Approach for Early Detection of Alzheimer’s Disease. IEEE Access, 11, (2023) 44650-44659. https://doi.org/10.1109/ACCESS.2023.3272482

N. Shoaip, A. Rezk, S. El-Sappagh, L. Alarabi, S. Barakat, M. M. Elmogy, A Comprehensive Fuzzy Ontology-Based Decision Support System for Alzheimer’s Disease Diagnosis. IEEE Access, 9, (2021) 31350-31372. https://doi.org/10.1109/ACCESS.2020.3048435

Z. Qu, T. Yao, X. Liu, G. Wang, A Graph Convolutional Network Based on Univariate Neurodegeneration Biomarker for Alzheimer’s Disease Diagnosis. IEEE Journal of Translational Engineering in Health and Medicine, 11, (2023) 405-416. https://doi.org/10.1109/JTEHM.2023.3285723

F.U.R. Faisal, G.R. Kwon, Automated Detection of Alzheimer ’s disease and Mild Cognitive Impairment Using Whole Brain MRI. IEEE Access, 10, (2022) 65055-65066. https://doi.org/10.1109/ACCESS.2022.3180073

C. S. Eke, E. Jammeh, X. Li, C. Carroll, S. Pearson, E. Ifeachor, Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines. IEEE Journal of Biomedical and Health Informatics, 25(1), (2021) 218-226. https://doi.org/10.1109/JBHI.2020.2984355

T. Habuza, N. Zaki, E. Mohamed, Y. Statsenko, Deviation from Model of Normal Aging in Alzheimer’s Disease: Application of Deep Learning to Structural MRI Data and Cognitive Tests. IEEE Access, 10, (2022) 53234-53249. https://doi.org/10.1109/ACCESS.2022.3174601

Y. Zhang, T. Liu, V. Lanfranchi and P. Yang, Explainable Tensor Multi-Task Ensemble Learning Based on Brain Structure Variation for Alzheimer’s Disease Dynamic Prediction. IEEE Journal of Translational Engineering in Health and Medicine, 11, (2023) 1-12. https://doi.org/10.1109/JTEHM.2022.3219775

G.P. Shukla, S. Kumar, S.K. Pandey, R. Agarwal, N. Varshney, A. Kumar, Diagnosis and detection of Alzheimer's disease using learning algorithm. Big Data Mining and Analytics, 6(4), (2023) 504-512. https://doi.org/10.26599/BDMA.2022.9020049

L. Wang, J. Sheng, Q. Zhang, R. Zhou, Z. Li, Y. Xin, Q. Zhang, Functional Brain Network Measures for Alzheimer’s Disease Classification. IEEE Access, 11, (2023) 111832 – 111845. https://doi.org/10.1109/ACCESS.2023.3323250

C.C. Fan, H. Yang, C. Zhang, L. Peng, X. Zhou, S. Liu, S. Chen, Z.G. Hou, Graph Reasoning Module for Alzheimer’s Disease Diagnosis: A Plug-and-Play Method. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, (2023) 4773-4780. https://doi.org/10.1109/TNSRE.2023.3337533

Y. Pusparani, C.Y. Lin, Y.K. Jan, F.Y. Lin, B.Y. Liau, P. Ardhianto, I. Farady, J.S. Rani Alex, J. Aparajeeta, W.H. Chao, C.W. Lung, Diagnosis of Alzheimer’s disease using convolutional neural network with select slices by landmark on Hippocampus in MRI images. IEEE Access, 11, (2023) 61688-61697. https://doi.org/10.1109/ACCESS.2023.3285115

A. AlMohimeed, R.M. Saad, S. Mostafa, N.M. El-Rashidy, S. Farrag, A. Gaballah, M.A. Elaziz, S. El-Sappagh, H. Saleh, Explainable artificial intelligence of multi-level stacking ensemble for detection of Alzheimer’s disease based on particle swarm optimization and the sub-scores of cognitive biomarkers. IEEE Access, 11, (2023) 123173-123193. https://doi.org/10.1109/ACCESS.2023.3328331

L. Wu, W. Zhang, S. Li, Y. Li, Y. Yuan, L. Huang, T. Cao, L. Fan, J. Chen, J. Wang, T. Liu, J. Wang, Transcranial alternating current stimulation improves memory function in Alzheimer’s mice by ameliorating abnormal gamma oscillation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, (2023) 2060-2068. https://doi.org/10.1109/TNSRE.2023.3265378

W. Liu, Q. Dong, S. Sun, J. Shen, K. Qian, B. Hu, Risk Prediction of Alzheimer’s Disease Conversion in Mild Cognitive Impaired Population Based on Brain Age Estimation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, (2023) 2468-2476. https://doi.org/10.1109/TNSRE.2023.3247590

A. Al-Sayed, M.M. Khayyat, N. Zamzami, Predicting Heart Disease Using Collaborative Clustering and Ensemble Learning Techniques. Applied Sciences, 13(24), (2023) 13278. https://doi.org/10.3390/app132413278

G. Chao, S. Sun, J. Bi, A Survey on Multiview Clustering. IEEE Transactions on Artificial Intelligence, 2(2), (2021) 146-168. https://doi.org/10.1109/TAI.2021.3065894

S. El Hajjar, F. Dornaika, F. Abdallah, Recognizing and detecting COVID-19 in chest X-ray images using constrained multi-view spectral clustering. Progress in Artificial Intelligence, (2024) 1-14. https://doi.org/10.1007/s13748-023-00312-x

Alzheimer’s Disease Neuroimaging Initiative. (n.d.). ADNI. https://adni.loni.usc.edu/

Y. Zhao, B. Ma, T. Che, Q. Li, D. Zeng, X. Wang, S. Li, Multi-view prediction of Alzheimer’s disease progression with end-to-end integrated framework. Journal of Biomedical Informatics, 125, (2022) 103978. https://doi.org/10.1016/j.jbi.2021.103978

P. Jiang, X. Wang, Q. Li, L. Jin, S. Li, Correlation-Aware Sparse and Low-Rank Constrained Multi-Task Learning for Longitudinal Analysis of Alzheimer’s Disease. IEEE Journal of Biomedical and Health Informatics, 23(4), (2019) 1450–1456. https://doi.org/10.1109/JBHI.2018.2885331

J. Zhou, J. Liu, V.A. Narayan, J. Ye, Alzheimer's Disease Neuroimaging Initiative. Modeling disease progression via multi-task learning. NeuroImage, 78, (2013) 233-248. https://doi.org/10.1016/j.neuroimage.2013.03.073

N. Bhagwat, J. Pipitone, A.N. Voineskos, M.M. Chakravarty, Alzheimer’s Disease Neuroimaging Initiative. An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures. Journal of Psychiatry and Neuroscience, 44(4), (2019) 246-260. https://doi.org/10.1503/jpn.180016

B. Lei, F. Jiang, S. Chen, D. Ni, T. Wang, Longitudinal analysis for disease progression via simultaneous multi-relational temporal-fused learning. Frontiers in aging neuroscience, 9, (2017) 6. https://doi.org/10.3389/fnagi.2017.00006