Alzheimer's disease Recognition Classification Study Using MRI Images Based on Deep Learning and Dual Multilayer Attention Mechanisms

Document Type : Original Paper

Authors

Chengdu University Of Information Technology

10.22038/ijmp.2025.87893.2542

Abstract

Introduction: Current deep learning-based computer-aided diagnosis (CAD) techniques face challenges in hierarchical feature extraction and computational efficiency. Traditional convolutional neural networks (CNN) often focus on local or single-scale information, neglecting global correlations of brain atrophy and multiscale pathological features. Additionally, the parameter explosion problem in deep networks limits model's generalization ability on small and medium-sized datasets. While the introduction of attention mechanisms has significantly improved feature extraction and enhanced CNN recognition capabilities, existing attention mechanisms are mostly single-scale, focusing on feature maps at specific hierarchical levels and ignoring the correlations between features of different layers.
Material and Methods: To address these issues, this study proposes a lightweight model combining a shallow feature pyramid CNN with a Dual Multi-level Attention (DMA) mechanism. Experiments using the public OASIS-1 dataset, which contains 86,437 MRI images across 4 categories, employ a focal loss function to handle class imbalance.
Results: The results show that the model including DMA outperforms both the baseline CNN and the single-scale attention mechanism in terms of accuracy (ACC), sensitivity (SEN), and specificity (SPE). Specifically, compared to CNN and CNN+CBAM: ACC improved by 3.33% and 1.26%, SEN improved by 13.2% and 0.9%, and SPE improved by 1%.
Conclusion: The model demonstrates significant advantages in distinguishing small-sample classes and differentiating between very mild dementia and normal controls, highlighting its superiority in fine-grained pathological discrimination.

Keywords

Main Subjects

References

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  1. Przedborski S, Vila M, Jackson-Lewis V. Neurodegeneration: what is it and where are we? J Clin Invest. 2003;111(1):3-10.
  2. Tufail AB, Ma YK, Zhang QN. Binary classification of Alzheimer’s disease using sMRI imaging modality and deep learning. J Digit Imaging. 2020;33(5):1073-90.
  3. Tiwari S, Atluri V, Kaushik A, et al. Alzheimer’s disease: pathogenesis, diagnostics, and therapeutics. Int J Nanomedicine. 2019;14:5541-54.
  4. De la Torre JC. Alzheimer’s disease is incurable but preventable. J Alzheimers Dis. 2010;20(3):861-70.
  5. Alzheimer’s Association. 2020 Alzheimer’s disease facts and figures. Alzheimers Dement. 2020;16(3):391-460.
  6. Bateman RJ, Xiong C, Benzinger TLS, Fagan AM, Cruchaga C, Goate AM, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med. 2012;367(9):795-804.
  7. Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A. 2004;101(13):4637-42.
  8. Guan H, Liu M. Domain adaptation for medical image analysis: a survey. IEEE Trans Biomed Eng. 2022;69(3):1173-85.
  9. Hwang S, et al. Multi-scale feature fusion with hierarchical attention for Alzheimer’s disease classification. Med Image Anal. 2021;72:102107.
  10. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018.
  11. Ma N, Zhang X, Zheng HT, Sun J. ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV); 2018.
  12. Tan M, Le QV. EfficientNet: rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946. 2019.
  13. Abedinzadeh Torghabeh F, Hosseini SA. Deep learning-based brain tumor segmentation in MRI images: a MobileNetV2-DeepLabV3+ approach. Iran J Med Phys. 2024;21(6):343-54.
  14. Li H, Wei Y, Li L, Tang X. Hierarchical feature extraction with local neural response for image recognition. IEEE Trans Cybern. 2013;43(2):412-24.
  15. Woo S, Park J, Lee JY, Kweon IS. CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV); 2018.
  16. Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018.
  17. Zhu X, Cheng D, Zhang Z, Lin S, Dai J. An empirical study of spatial attention mechanisms in deep networks. In: Proceedings of the IEEE International Conference on Computer Vision; 2019.
  18. Mu S, Shan S, Li L, Yang Z, Fang Z, Chen Z, et al. DMA-HPCNet: dual multi-level attention hybrid pyramid convolution neural network for Alzheimer’s disease classification. IEEE Trans Neural Syst Rehabil Eng. 2024.
  19. 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. J Cogn Neurosci. 2007;19(9):1498-507.
  20. Jack CR Jr, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et al. The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685-91.
  21. Roncero-Parra C, Parreo-Torres A, Sánchez-Reolid R, Morales-Sánchez J, de la Torre-Díez I, López-Coronado M. Inter-hospital moderate and advanced Alzheimer’s disease detection through convolutional neural networks. Heliyon. 2024;10(4):e26298.
  22. Lim JS. Zoom-in neural network deep-learning model for Alzheimer’s disease assessments. Sensors (Basel). 2022;22(22):8887.

 

 

 

Iranian Journal of Medical Physics
Volume 22, Issue 4 - Serial Number 4
July and August 2025
Pages 270-278

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History

  • Receive Date: 30 April 2025
  • Revise Date: 06 October 2025
  • Accept Date: 07 October 2025

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