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.
Index Terms—Augumented images, perspective transformation, B mode ultrasound images, cirrhosis liver classification, computer-aided diagnosis system, convolution neural networks, over-training problem.
Yoshihiro Mitani is with the National Institute of Technology, Ube College, Japan (e-mail: firstname.lastname@example.org).
Robert B. Fisher is with the University of Edinburgh, UK.
Yusuke Fujita, Yoshihiko Hamamoto, and Isao Sakaida are with the Yamaguchi University, Japan.
Cite: Yoshihiro Mitani, Robert B. Fisher, Yusuke Fujita, Yoshihiko Hamamoto, and Isao Sakaida, "Cirrhosis Liver Classification on B-Mode Ultrasound Images by Convolution Neural Networks with Augmented Images," International Journal of Machine Learning and Computing vol. 10, no. 6, pp. 723-728, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).