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International Conference on Intelligent Science and Big Data Engineering

IScIDE 2017: Intelligence Science and Big Data Engineering pp 191–199Cite as

Jointly Using Deep Model Learned Features and Traditional Visual Features in a Stacked SVM for Medical Subfigure Classification

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10559))

Abstract

Classification of diagnose images and illustrations in the literature is a major challenge towards automated literature review and retrieval. Although being widely recognized as the most successful image classification technique, deep learning models, however, may need to be complemented by traditional visual features to solve this problem, in which there are intra-class variation, inter-class similarity and a small training dataset. In this paper, we propose an approach to classifying diagnose images and biomedical publication illustrations. This algorithm jointly uses the image representations learned by three pre-trained deep convolutional neural network models and ten types of traditional visual features in a stacked support vector machine (SVM) classifier. We have evaluated this algorithm on the ImageCLEF 2016 Subfigure Classification dataset and achieved an accuracy of 85.62%, which is higher than the top performance of purely visual approaches in this challenge.

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Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grants 61471297 and 61771397, and in part by the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University. We appreciate the efforts devoted by the organizers of the ImageCLEF2016 Medical Image Classification Challenge to collect and share the data for comparing algorithms of classifying diagnose images and illustrations in the biomedical literature.

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Authors and Affiliations

  1. Shaanxi Key Lab of Speech and Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, 710072, People’s Republic of China

    Hongyu Wang, Jianpeng Zhang & Yong Xia

  2. Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science and Technology, Northwestern Polytechnical University, Xi’an, 710072, People’s Republic of China

    Yong Xia

Authors
  1. Hongyu Wang
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  2. Jianpeng Zhang
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  3. Yong Xia
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Corresponding author

Correspondence to Yong Xia .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Yi Sun

  2. Dalian University of Technology, Dalian, China

    Huchuan Lu

  3. Dalian University of Technology, Dalian, China

    Lihe Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  5. Beijing Institute of Technology, Beijing, China

    Hua Huang

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Wang, H., Zhang, J., Xia, Y. (2017). Jointly Using Deep Model Learned Features and Traditional Visual Features in a Stacked SVM for Medical Subfigure Classification. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_17

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  • DOI: https://doi.org/10.1007/978-3-319-67777-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67776-7

  • Online ISBN: 978-3-319-67777-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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