- Yoshihiro Mitani
- Robert Fisher
- Yusuke Fujita
- Yoshihiko Hamamoto
- Isao Sakaida
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Accepted author manuscript, 748 KB, PDF document
Licence: All Rights Reserved
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Final published version, 925 KB, PDF document
Licence: Creative Commons: Attribution (CC-BY)
Original language | English |
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Article number | 996 |
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Pages (from-to) | 723-728 |
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Number of pages | 6 |
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Journal | International Journal of Machine Learning and Computing |
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Volume | 10 |
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Issue number | 6 |
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DOIs | |
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Publication status | Published - 1 Nov 2020 |
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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.
ID: 160190535