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Research of spatial context convolutional neural networks for early diagnosis of Alzheimer’s disease

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Abstract

The early and effective diagnosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI) has received increasing attention in recent years. However, currently available deep learning methods often ignore the contextual spatial information contained in structural MRI images used for early diagnosis and classification of Alzheimer’s disease. This may lead us to miss important structural details by failing to adequately capture the potential connections between each slice and its neighboring slices. This lack of contextual information may cause the accuracy of the network model to suffer, which in turn affects its generalization ability and application in real-life scenarios. To explore deeper the connection between spatial context slices, this research is designed to develop a new network model to effectively detect or predict AD by digging into the deeper spatial contextual structural information. In this paper, we design a spatial context network based on 3D convolutional neural network to learn the multi-level structural features of brain MRI images for AD classification. The experimental results show that the model has good stability, accuracy and generalization ability. Our experimental method had a classification accuracy of 92.6% in the AD/CN comparison, 74.9% in the AD/MCI comparison, and 76.3% in the MCI/CN comparison. In addition, this paper demonstrates the effectiveness of the proposed network model through ablation experiments.

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Data availability

The datasets generated and/or analyzed during this study are available in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database publicly available at [https://adni.loni.usc.edu/].

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Acknowledgements

This work was supported by the Key Project of Zhejiang Provincial Natural Science Foundation under Grant LD21F020001, Grant LSZ19F020001, Grant Z20F020022, and the National Natural Science Foundation of China under Grant U1809209, Grant 62072340, Grant 61972187, Grant 61772254, and the Major Project of Wenzhou Natural Science Foundation under Grant ZY2019020, and the Natural Science Foundation of Fujian Province under Grant 2020J02024. We acknowledge the efforts and constructive comments of respected editors and anonymous reviewers.

Funding

This study was supported by the Key Project of Zhejiang Provincial Natural Science Foundation under Grant LD21F020001, Grant LSZ19F020001, Grant Z20F020022, and the National Natural Science Foundation of China under Grant U1809209, Grant 62072340, Grant 61972187, Grant 61772254, and the Major Project of Wenzhou Natural Science Foundation under Grant ZY2019020, and the Natural Science Foundation of Fujian Province under Grant 2020J02024.

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Author notes

  1. Zuoyong Li, Hui Huang, Libin Gao, Minghai Xu and Zhongyi Hu have contributed equally to this work.

Authors and Affiliations

  1. College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, Zhejiang, China

    Yinsheng Tong, Hui Huang, Libin Gao & Zhongyi Hu

  2. Key Laboratory of Intelligent Image Processing and Analysis, Wenzhou, 325035, Zhejiang, China

    Yinsheng Tong, Hui Huang, Libin Gao, Minghai Xu & Zhongyi Hu

  3. College of Computer and Control Engineering, Minjiang University, Fuzhou, 350108, Fujian, China

    Zuoyong Li

  4. College of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou, 325035, Zhejiang, China

    Minghai Xu

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  1. Yinsheng Tong
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Contributions

Conceptualization was performed by Yinsheng Tong. Yinsheng Tong contributed to the development. Yinsheng Tong contributed to the testing/formal analysis. Zuoyong Li, Hui Huang, Minghai XU and Zhongyi Hu acquired the funding. Yinsheng Tong and Zhongyi Hu contributed to the investigation. Yinsheng Tong assisted in the investigation. Zhongyi Hu was involved in the validation. Yinsheng Tong contributed to writing—original draft. Zhongyi Hu and Libin Gao contributed to writing—review and editing. All authors read and approved the final manuscript.

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Correspondence to Minghai Xu or Zhongyi Hu.

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Tong, Y., Li, Z., Huang, H. et al. Research of spatial context convolutional neural networks for early diagnosis of Alzheimer’s disease. J Supercomput 80, 5279–5297 (2024). https://doi.org/10.1007/s11227-023-05655-9

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