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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2019: Advances in Knowledge Discovery and Data Mining pp 449–460Cite as

Discovering Senile Dementia from Brain MRI Using Ra-DenseNet

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11441))

Abstract

With the rapid development of medical industry, there is a growing demand for disease diagnosis using machine learning technology. The recent success of deep learning brings it to a new height. This paper focuses on application of deep learning to discover senile dementia from brain magnetic resonance imaging (MRI) data. In this work, we propose a novel deep learning model based on Dense convolutional Network (DenseNet), denoted as ResNeXt Adam DenseNet (Ra-DenseNet), where each block of DenseNet is modified using ResNeXt and the adapter of DenseNet is optimized by Adam algorithm. It compresses the number of the layers in DenseNet from 121 to 40 by exploiting the key characters of ResNeXt, which reduces running complexity and inherits the advantages of Group Convolution technology. Experimental results on a real-world MRI data set show that our Ra-DenseNet achieves a classification accuracy with 97.1\(\%\) and outperforms the existing state-of-the-art baselines (i.e., LeNet, AlexNet, VGGNet, ResNet and DenseNet) dramatically.

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Acknowledgment

This work was supported by the National Natural Science Foundation of China (No. 61572407) and the Seeding Project of Scientific and Technological Innovation in Sichuan Province of China (No. 2018102).

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Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China

    Xiaobo Zhang, Yan Yang, Tianrui Li, Hao Wang & Ziqing He

Authors
  1. Xiaobo Zhang
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  2. Yan Yang
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  3. Tianrui Li
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  4. Hao Wang
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  5. Ziqing He
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Corresponding author

Correspondence to Yan Yang .

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Editors and Affiliations

  1. Hong Kong University of Science and Technology, Hong Kong, China

    Qiang Yang

  2. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  3. University of Macau, Taipa, Macau, China

    Zhiguo Gong

  4. Southeast University, Nanjing, China

    Min-Ling Zhang

  5. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Sheng-Jun Huang

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Zhang, X., Yang, Y., Li, T., Wang, H., He, Z. (2019). Discovering Senile Dementia from Brain MRI Using Ra-DenseNet. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11441. Springer, Cham. https://doi.org/10.1007/978-3-030-16142-2_35

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  • DOI: https://doi.org/10.1007/978-3-030-16142-2_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16141-5

  • Online ISBN: 978-3-030-16142-2

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