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Vol. 32, No. 8(2), S&M2292

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Online: ISSN 2435-0869
Sensors and Materials
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Sensors and Materials, Volume 32, Number 8(2) (2020)
Copyright(C) MYU K.K.
pp. 2745-2753
S&M2297 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.2801
Published: August 20, 2020

Classification of Hepatocellular Carcinoma and Liver Abscess by Applying Neural Network to Ultrasound Images [PDF]

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

(Received January 13, 2020; Accepted May 21, 2020)

Keywords: liver abscess, hepatocellular carcinoma (HCC), neural network (NN), ultrasound images, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRM)

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.

Corresponding author: Chien-Tien Su


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Cite this article
Sendren Sheng-Dong Xu, Chun-Chao Chang, Chien-Tien Su, Pham Quoc Phu, Tifany Inne Halim, and Shun-Feng Su, Classification of Hepatocellular Carcinoma and Liver Abscess by Applying Neural Network to Ultrasound Images, Sens. Mater., Vol. 32, No. 8, 2020, p. 2745-2753.



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