Classification with CNN features and SVM on Embedded DSP Core for Colorectal Magnified NBI Endoscopic Video Image

DOI Web Site 参考文献13件
  • ODAGAWA Masayuki
    Research Institute for Nanodevice and Bio Systems, Hiroshima University Cadence Design Systems Japan
  • OKAMOTO Takumi
    Cadence Design Systems Japan
  • KOIDE Tetsushi
    Research Institute for Nanodevice and Bio Systems, Hiroshima University
  • TAMAKI Toru
    Department of Computer Science, Nagoya Institute of Technology
  • YOSHIDA Shigeto
    Department of Gastroenterology, Medical Corporation JR Hiroshima Hospital
  • MIENO Hiroshi
    Department of Gastroenterology, Medical Corporation JR Hiroshima Hospital
  • TANAKA Shinji
    Department of Endoscopy and Medicine Graduate School of Biomedical and Health Science, Hiroshima University

抄録

<p>In this paper, we present a classification method for a Computer-Aided Diagnosis (CAD) system in a colorectal magnified Narrow Band Imaging (NBI) endoscopy. In an endoscopic video image, color shift, blurring or reflection of light occurs in a lesion area, which affects the discrimination result by a computer. Therefore, in order to identify lesions with high robustness and stable classification to these images specific to video frame, we implement a CAD system for colorectal endoscopic images with the Convolutional Neural Network (CNN) feature and Support Vector Machine (SVM) classification on the embedded DSP core. To improve the robustness of CAD system, we construct the SVM learned by multiple image sizes data sets so as to adapt to the noise peculiar to the video image. We confirmed that the proposed method achieves higher robustness, stable, and high classification accuracy in the endoscopic video image. The proposed method also can cope with differences in resolution by old and new endoscopes and perform stably with respect to the input endoscopic video image.</p>

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