Classifier Approaches for Liver Steatosis using Ultrasound Images

This paper presents a semi-automatic classification approach to evaluate steatotic liver tissues using B-scan ultrasound images. Several features have been extracted and used in three different classifiers, such as Artificial Neural Networks (ANN), Support Vector Machines (SVM) and k-Nearest Neighbo...

Full description

Saved in:
Bibliographic Details
Published in:Procedia technology 2012, Vol.5, p.763-770
Main Authors: Andrade, Andreia, Silva, José Silvestre, Santos, Jaime, Belo-Soares, Pedro
Format: Article
Language:English
Subjects:
Classifier
Feature Extraction
Liver Steatosis
Ultrasound
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a semi-automatic classification approach to evaluate steatotic liver tissues using B-scan ultrasound images. Several features have been extracted and used in three different classifiers, such as Artificial Neural Networks (ANN), Support Vector Machines (SVM) and k-Nearest Neighbors (kNN). The classifiers were trained using the 10-cross validation method. A feature selection method based on stepwise regression was also exploited resulting in better accuracy predictions. The results showed that the SVM have a slightly higher performance than the kNN and the ANN, appearing as the most relevant one to be applied to the discrimination of pathologic tissues in clinical practice.
ISSN:2212-0173
2212-0173
DOI:10.1016/j.protcy.2012.09.084