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

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)2745-2753
Number of pages9
JournalSensors and Materials
Volume32
Issue number8
DOIs
Publication statusPublished - Aug 2020

Keywords

  • Gray-level co-occurrence matrix (GLCM)
  • Gray-level run-length matrix (GLRM)
  • Hepatocellular carcinoma (HCC)
  • Liver abscess
  • Neural network (NN)
  • Ultrasound images

ASJC Scopus subject areas

  • Instrumentation
  • Materials Science(all)

Fingerprint Dive into the research topics of 'Classification of hepatocellular carcinoma and liver abscess by applying neural network to ultrasound images'. Together they form a unique fingerprint.

  • Cite this