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Advances in Bioinformatics
Volume 2014 (2014), Article ID 708279, 14 pages
http://dx.doi.org/10.1155/2014/708279
Research Article

Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images

1Systems Biomedicine Division, Haffkine Institute for Training Research and Testing, Parel, Mumbai, Maharashtra 400012, India
2Research Advisory Council, Haffkine Institute for Training Research and Testing, Parel, Mumbai, Maharashtra 400012, India
3Nuclear Medicine Department, Jaslok Hospital and Research Centre, Pedder Road, Mumbai, Maharashtra 400026, India
4Ultrasound Department, Jaslok Hospital and Research Centre, Pedder Road, Mumbai, Maharashtra 400026, India

Received 31 May 2014; Revised 6 August 2014; Accepted 7 August 2014; Published 16 September 2014

Academic Editor: Huixiao Hong

Copyright © 2014 Karthik Kalyan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data.