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Discriminative Feature Selection by Optimal Manifold Search for Neoplastic Image Recognition

  • Hayato ItohEmail author
  • Yuichi Mori
  • Masashi Misawa
  • Masahiro Oda
  • Shin-Ei Kudo
  • Kensaku Mori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11132)

Abstract

An endocytoscope provides ultramagnified observation that enables physicians to achieve minimally invasive and real-time diagnosis in colonoscopy. However, great pathological knowledge and clinical experiences are required for this diagnosis. The computer-aided diagnosis (CAD) system is required that decreases the chances of overlooking neoplastic polyps in endocytoscopy. Towards the construction of a CAD system, we have developed texture-feature-based classification between neoplastic and non-neoplastic images of polyps. We propose a feature-selection method that selects discriminative features from texture features for such two-category classification by searching for an optimal manifold. With an optimal manifold, where selected features are distributed, the distance between two linear subspaces is maximised. We experimentally evaluated the proposed method by comparing the classification accuracy before and after the feature selection for texture features and deep-learning features. Furthermore, we clarified the characteristics of an optimal manifold by exploring the relation between the classification accuracy and the output probability of a support vector machine (SVM). The classification with our feature-selection method achieved 84.7% accuracy, which is 7.2% higher than the direct application of Haralick features and SVM.

Keywords

Feature selection Manifold learning Texture feature Convolutional neural network Endocytoscopic images Automated pathological diagnosis 

Notes

Acknowledgements

Parts of this research were supported by the Research on Development of New Medical Devices from the Japan Agency for Medical Research and Development (No. 18hk0102034h0103), and MEXT KAKENHI (No. 26108006, No. 17H00867).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hayato Itoh
    • 1
    Email author
  • Yuichi Mori
    • 2
  • Masashi Misawa
    • 2
  • Masahiro Oda
    • 1
  • Shin-Ei Kudo
    • 2
  • Kensaku Mori
    • 1
    • 3
    • 4
  1. 1.Graduate School of InformaticsNagoya UniversityNagoyaJapan
  2. 2.Digestive Disease CenterShowa University Northern Yokohama HospitalYokohamaJapan
  3. 3.Information Technology CenterNagoya UniversityNagoyaJapan
  4. 4.Research Center for Medical BigdataNational Institute of InformaticsTokyoJapan

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