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Cirrhosis liver classification on B-mode ultrasound images by convolution neural networks with augmented images

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Yoshihiro Mitani
  • Robert Fisher
  • Yusuke Fujita
  • Yoshihiko Hamamoto
  • Isao Sakaida

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Original languageEnglish
Title of host publicationProceedings of the 2019 International Conference on Machine Learning and Machine Intelligence
PublisherACM
Number of pages5
Publication statusAccepted/In press - 26 Jul 2019
Event2nd International Conference on Machine Learning and Machine Intelligence - Jakarta, Indonesia
Duration: 18 Sep 201920 Sep 2019
http://mlmi.net/

Conference

Conference2nd International Conference on Machine Learning and Machine Intelligence
Abbreviated titleMLMI 2019
CountryIndonesia
CityJakarta
Period18/09/1920/09/19
Internet address

Abstract

In the medical imaging field, it is desirable to develop computer-aided diagnosis(CAD) systems. They are useful as a second opinion, and to objectively and quantitatively make diagnoses. In this study, we focus on liver ultrasound images. The cirrhosis liver is expected to progress to a liver cancer in the worst case. Therefore, we are investigating a CAD system to identify the cirrhosis liver sooner. In this paper, in order to classify cirrhosis or normal liver on regions of interest(ROIs) image from B-mode ultrasound images, we have proposed to use a convolution neural network(CNN). CNNs are one of promising techniques for medical image recognition. In a previous study, we tried to classify the cirrhosis liver using a Gabor features based method, a higher order local auto-correlation(HLAC) feature based approach and an improved version. However, the classification performance of our preliminary experimental results were poor. The average error rates were still over 40%. In order to more accurately classify the cirrhosis liver, we have explored the use of the CNNs. The experimental results show the effectiveness of the CNNs. Furthermore, by a data augmentation technique, the classification performance of the CNNs is improved.

Event

2nd International Conference on Machine Learning and Machine Intelligence

18/09/1920/09/19

Jakarta, Indonesia

Event: Conference

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