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Alzheimer’s disease diagnosis based on long-range dependency mechanism using convolutional neural network

  • 1210: Computer Vision for Clinical Images
  • Published:
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Abstract

Being able to collect rich morphological information of brain, structural magnetic resonance imaging (MRI) is popularly applied to computer-aided diagnosis of Alzheimer’s disease (AD). Conventional methods for AD diagnosis are labor-intensive and typically depend on a substantial amount of hand-crafted features. In this paper, we propose a novel framework of convolutional neural network that aims at identifying AD or normal control, and mild cognitive impairment or normal control. The centerpiece of our method are pseudo-3D block and expanded global context block which are integrated into residual block of backbone in a cascaded manner. To be specific, we transfer pseudo-3D block in the video feature representation to extract structural MRI features. Besides, we extend the 2D global context block to the 3D model which can effectively combine the features and capture the latent associations, while simulate the global context in each dimension of structural MRI results. With the preprocessed structural MRI used as the input of the overall network, our method is capable of constructing a latent representation with multiple residual blocks to promote the classification accuracy. Intrinsically, our method reduces the complexity of conventional 3D convolutional network model applied to AD diagnosis and improves the classification accuracy over the baseline. Furthermore, our network can fully take advantage of the deep 3D convolutional neural network for automatic feature extraction and representation, and thus avoids the limitation of low processing efficiency caused by the preprocessing procedure in which a specific area needs to be annotated in advance. Experimental results on Alzheimer’s Disease Neuroimaging Initiative database indicate that our proposed method reports accuracy of 89.29% on the AD/NC and 87.57% on the mild cognitive impairment/NC, whilst our approach achieves the 0.5% improvement of accuracy compared with the backbone.

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Notes

  1. http://adni.loni.usc.edu/

  2. http://www.neuro.uni-jena.de/cat/

  3. https://www.fil.ion.ucl.ac.uk/spm-statistical-parametric-mapping/

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant 61971273, Grant 61877038, Grant 61702251, Grant 61703096, the Key Research and Development Program in Shaanxi Province of China under Grant 2021GY-032 and the Fundamental Research Funds for the Central Universities under Grant GK202003077, Grant GK202105006. The authors would like to gratefully thank NVIDIA Corporationfor for the support of the Titan XP GPU used in our work.

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Authors and Affiliations

  1. Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, 710062, China

    Zhao Pei

  2. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Zhao Pei, Yuanshuai Gou, Miao Ma, Min Guo & Yuli Chen

  3. School of Mathematics, Northwest University, Xi’an, 710127, China

    Chengcai Leng

  4. School of Computer Science, Nanjing Normal University, Nanjing, 210046, China

    Jun Li

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  1. Zhao Pei
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Contributions

Conceptualization, Z.P.; Methodology, M.M. and M.G.; Software, Z.P. and Y.G.; Supervision, M.M.; Resources, M.G. and C.L.; Data curation, Y.G. and Y.C.; Writing—original draft, Z.P. and Y.G.; Writing—review and editing, M.M., C.L. and J.L. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Zhao Pei.

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Pei, Z., Gou, Y., Ma, M. et al. Alzheimer’s disease diagnosis based on long-range dependency mechanism using convolutional neural network. Multimed Tools Appl 81, 36053–36068 (2022). https://doi.org/10.1007/s11042-021-11279-z

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  • DOI: https://doi.org/10.1007/s11042-021-11279-z

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