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An Efficient Lightweight Network Based on Magnetic Resonance Images for Predicting Alzheimer's Disease

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  • Boan Ji

    (Anhui University, China)

  • Huabin Wang

    (Anhui University, China)

  • Mengxin Zhang

    (Anhui University, China)

  • Borun Mao

    (Anhui University, China)

  • Xuejun Li

    (Anhui University, China)

Abstract

Brain magnetic resonance images (MRI) are widely used for the classification of Alzheimer's disease (AD). The size of 3D images is, however, too large. Some of the sliced image features are lost, which results in conflicting network size and classification performance. This article uses key components in the transformer model to propose a new lightweight method, ensuring the lightness of the network and achieving highly accurate classification. First, the transformer model is imitated by using image patch input to enhance feature perception. Second, the Gaussian error linear unit (GELU), commonly used in transformer models, is used to enhance the generalization ability of the network. Finally, the network uses MRI slices as learning data. The depthwise separable convolution makes the network more lightweight. Experiments are carried out on the ADNI public database. The accuracy rate of AD vs. normal control (NC) experiments reaches 98.54%. The amount of network parameters is 1.3% of existing similar networks.

Suggested Citation

  • Boan Ji & Huabin Wang & Mengxin Zhang & Borun Mao & Xuejun Li, 2022. "An Efficient Lightweight Network Based on Magnetic Resonance Images for Predicting Alzheimer's Disease," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 18(1), pages 1-18, January.
    • Handle: RePEc:igg:jswis0:v:18:y:2022:i:1:p:1-18
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    1. Adnen Mahmoud & Mounir Zrigui, 2020. "Distributional Semantic Model Based on Convolutional Neural Network for Arabic Textual Similarity," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 14(1), pages 35-50, January.
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