A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

Gletsos, Miltiades; Mougiakakou, Stavroula G; Matsopoulos, George K; Nikita, Konstantina S; Nikita, Alexandra S; Kelekis, Dimitrios (2003). A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier. IEEE transactions on information technology in biomedicine, 7(3), pp. 153-62. New York, N.Y.: Institute of Electrical and Electronics Engineers IEEE 10.1109/TITB.2003.813793

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In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.

Item Type:	Journal Article (Original Article)
Division/Institute:	04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition
UniBE Contributor:	Mougiakakou, Stavroula
ISSN:	1089-7771
Publisher:	Institute of Electrical and Electronics Engineers IEEE
Language:	English
Submitter:	Factscience Import
Date Deposited:	04 Oct 2013 15:23
Last Modified:	05 Dec 2022 14:25
Publisher DOI:	10.1109/TITB.2003.813793
PubMed ID:	14518728
Web of Science ID:	000185338100002
URI:	https://boris.unibe.ch/id/eprint/37262 (FactScience: 207286)