STM Journals Peer-reviewed research from Consortium e-Learning Network

Research and Reviews: A Journal of Neuroscience Review Article

A Dual-Model Deep Learning Framework for Early Alzheimer’s Detection Using Clinical Data and Neuroimaging with Architectural Performance Analysis

  1. Abha Jain Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology Management & Gramothan, Jaipur
  2. Sohan Lal Gupta Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology Management & Gramothan, Jaipur
  3. Megha Gupta Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology Management & Gramothan, Jaipur
  4. Mithlesh Arya Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology Management & Gramothan, Jaipur
  5. Veena Yadav Department of Computer Science and Engineering, Poornima College of Engineering, Jaipur

Abstract

Alzheimer’s disease (AD) poses a significant global health challenge due to its increasing prevalence and the absence of definitive cures. Early diagnosis is crucial for effective intervention and management. This study presents a dual-model deep learning framework for the early detection and classification of AD using both structured clinical data and neuroimaging datasets. Model 1 utilizes a greedy layer-wise autoencoder approach applied to structured data, achieving optimal binary classification accuracy of 95.8% with a four-layer configuration. Model 2 employs EfficientNet-B0 via transfer learning to classify MRI brain scans across multiple AD stages, reaching accuracies of up to 94%. Comparative analysis with existing state-of-the-art models validates the effectiveness of both approaches. Additionally, this paper highlights how network architecture, depth, and input modality significantly impact diagnostic performance. The findings advocate for the strategic design of AI models tailored to clinical applications and set the foundation for future multimodal, interpretable, and scalable Alzheimer’s diagnostic tools.

Keywords

References (35)

  1. Jain R, Jain N, Aggarwal A, Hemanth DJ. Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. Cognitive Systems Research. 2019;57:147-159. doi:10.1016/j.cogsys.2018.12.015
  2. Suganthe RC, Geetha M, Sreekanth GR, Gowtham K, Deepakkumar S, Elango R. Multiclass classification of Alzheimer’s disease using hybrid deep convolutional neural network. Nat Volatiles Essent Oils J. 2021:145–153.
  3. Chitradevi D, Prabha S. Analysis of brain sub regions using optimization techniques and deep learning method in Alzheimer disease. Applied Soft Computing. 2020;86:105857. doi:10.1016/j.asoc.2019.105857
  4. Hosseini-Asl E, Gimel’farb G, El-Baz A. Alzheimer’s disease diagnostics by a deeply supervised adaptable 3D convolutional network. arXiv. 2016;arXiv:1607.00556.
  5. Saratxaga CL, Moya I, Picón A, Acosta M, Moreno-Fernandez-de-Leceta A, Garrote E, et al. MRI deep learning-based solution for Alzheimer’s disease prediction. J Pers Med. 2021;11(9):902.
  6. Khan A, Sohail A, Zahoora U, Qureshi AS. A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review. 2020;53(8):5455-5516. doi:10.1007/s10462-020-09825-6
  7. Yildirim M, Cinar A. Classification of Alzheimer’s disease MRI images with CNN-based hybrid method. Ing Syst Inf. 2020;25(4):413–418.
  8. Johnson KA, Fox NC, Sperling RA, Klunk WE. Brain imaging in Alzheimer disease. Cold Spring Harb Perspect Med. 2012;2(4):a006213.
  9. Alroobaea R, Mechti S, Haoues M, Rubaiee S, Ahmed A, Andejany M, et al. Alzheimer’s disease early detection using machine learning techniques. 2021.
  10. Fu’adah YN, Wijayanto I, Pratiwi NKC, Taliningsih FF, Rizal S, Pramudito MA. Automated classification of Alzheimer’s disease based on MRI image processing using convolutional neural network with AlexNet architecture. J Phys Conf Ser. 2021;1844(1):012020.
  11. Helaly HA, Badawy M, Haikal AY. Deep learning approach for early detection of Alzheimer’s disease. Cogn Comput. 2021:1–17.
  12. Górriz JM, Álvarez-Illán I, Álvarez-Marquina A, Arco JE, Atzmueller M, Ballarini F, et al. Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends. Inf Fusion. 2023;100:101945.
  13. Martínez-Murcia FJ, Górriz JM, Ramírez J, Ortiz A. Convolutional autoencoders for feature extraction in Alzheimer’s disease using neuroimaging biomarkers. Neurocomputing. 2018;272:1–10. doi:10.1016/j.neucom.2017.06.052.
  14. Basheer S, Bhatia S, Sakri SB. Computational modeling of dementia prediction using deep neural network: Analysis on OASIS dataset. IEEE Access. 2021;9:42449–42462.
  15. Qiu F, Berglund J, Jensen JR, Thakkar P, Ren D. Speckle noise reduction in SAR imagery using a local adaptive median filter. GISci Remote Sens. 2004;41(3):244–266.
  16. Sandoub G, Atta R, Ali HA, Abdel-Kader RF. A low-light image enhancement method based on bright channel prior. 2021.
  17. Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6(1):60.
  18. Prajapati R, Khatri U, Kwon GR. An efficient deep neural network binary classifier for Alzheimer’s disease classification. In: 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC); 2021 Apr. IEEE; 2021. p.231–234.
  19. Islam J, Zhang Y. Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain Inform. 2018;5:2.
  20. Singh SP, Wang L, Gupta S, Goli H, Padmanabhan P, Gulyás B. 3D deep learning on medical images: A review. Sensors (Basel). 2020;20(18):5097.
  21. Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: An overview and application in radiology. Insights Imaging. 2018;9:611–629.
  22. Kundaram SS, Pathak KC. Deep Learning-Based Alzheimer Disease Detection. Lecture Notes in Electrical Engineering. 2020:587-597. doi:10.1007/978-981-15-5546-6_50
  23. Ding Y, Sohn JH, Kawczynski MG, Trivedi H, Harnish R, Jenkins NW, et al. A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain. Radiology. 2019;290(2):456-464. doi:10.1148/radiol.2018180958
  24. Nawaz H, Maqsood M, Afzal S, Aadil F, Mehmood I, Rho S. A deep feature-based real-time system for Alzheimer disease stage detection. Multimedia Tools and Applications. 2020;80(28-29):35789-35807. doi:10.1007/s11042-020-09087-y
  25. Liu M, Zhang J, Adeli E, Shen D. Deep hierarchical multi-modal feature representation for Alzheimer’s disease diagnosis. Hum Brain Mapp. 2020;41(9):2487–2501. doi:10.1002/hbm.24970.
  26. Spasov S, Passamonti L, Duggento A, Liò P, Toschi N. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. NeuroImage. 2019;189:276-287. doi:10.1016/j.neuroimage.2019.01.031
  27. Sarraf S, Tofighi G. Classification of Alzheimer’s disease structural MRI data by deep learning convolutional neural networks. arXiv. 2016;arXiv:1607.06583.
  28. Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL. Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults. Journal of Cognitive Neuroscience. 2007;19(9):1498-1507. doi:10.1162/jocn.2007.19.9.1498
  29. Jack CR, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et al. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging. 2008;27(4):685-691. doi:10.1002/jmri.21049
  30. Jack CR, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimer's & Dementia. 2018;14(4):535-562. doi:10.1016/j.jalz.2018.02.018
  31. Abrol A, Bhatt H, Rashid B, Calhoun VD. Deep residual network for Alzheimer’s classification using neuroimaging biomarkers. Neuroimage. 2023;267:119843. doi:10.1016/j.neuroimage.2023.119843.
  32. Zavaliangos-Petropulu A, Nir TM, Thomopoulos SI, et al. AI-enabled multimodal MRI analysis for AD diagnosis: A reproducibility study. Brain Imaging Behav. 2022;16(1):143–154. doi:10.1007/s11682-021-00531-w.
  33. Gupta S, Gupta SL, Chauhan R, Joshi K, Sharma AK, Prabha C. Detection of Moving Objects in Two Dimensional Plane By Hand Gesture Recognition Using Convolution Neural Networks. 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. 2024:1-5. doi:10.1109/otcon60325.2024.10687923
  34. Ng YS, Xue W, Wang W, Qi P. Convolutional neural networks for food image recognition: An experimental study. In: Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management; 2019 Oct. p.33–41.
  35. Sarraf S, DeSouza DD, Anderson J, Tofighi G; Alzheimer’s Disease Neuroimaging Initiative. DeepAD: Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI. bioRxiv. 2016:070441.
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