Classification of hepatocellular carcinoma and liver abscess by applying neural network to ultrasound images

Sendren Sheng-Dong Xu, Chun Chao Chang, Chien Tien Su, Pham Quoc Phu, Tifany Inne Halim, Shun Feng Su

研究成果: 雜誌貢獻文章同行評審

1 引文 斯高帕斯(Scopus)

摘要

In diagnostic ultrasound, an ultrasound transducer converts an electrical signal into an ultrasound pulse, which enters the tissue from the body surface. At the surface, an echo appears. The probe senses and receives the echo, and all the echoes are converted back to signals and graphics, which can be analyzed by medical staff. We studied the neural network (NN)-based classification of hepatocellular carcinoma (HCC) and liver abscess using texture features of ultrasound images. From 79 cases of liver diseases (44 liver cancer and 35 liver abscess cases), we extracted 52 features of the gray-level co-occurrence matrix (GLCM) and 44 features of the gray-level run-length matrix (GLRLM), giving a total of 96 features. We used three feature selection models to distinguish these two liver diseases: Sequential forward selection (SFS), sequential backward selection (SBS), and F-score. We proved that our developed system can be used to classify liver cancer and liver abscess using an NN with an accuracy of 88.375%, which can provide diagnostic assistance for inexperienced clinicians.

原文英語
頁(從 - 到)2745-2753
頁數9
期刊Sensors and Materials
32
發行號8
DOIs
出版狀態已發佈 - 八月 2020

ASJC Scopus subject areas

  • 儀器
  • 材料科學(全部)

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