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.
|Number of pages||6|
|Journal||International Journal of Machine Learning and Computing|
|Publication status||Published - 1 Nov 2020|