An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images.

Abstract:

OBJECTIVE:This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images. METHODS:For the design of a neural network classifier, a Multi Objective Genetic Algorithm (MOGA) framework is used to determine the architecture of the classifier, its corresponding parameters and input features by maximizing the classification precision, while ensuring generalization. This approach considers a large number of input features, comprising first and second order pixel intensity statistics, as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line. RESULTS:Values of specificity of 98% and sensitivity of 98% were obtained, at pixel level, by an ensemble of non-dominated models generated by MOGA, in a set of 150 CT slices (1,867,602pixels), marked by a NeuroRadiologist. This approach also compares favorably at a lesion level with three other published solutions, in terms of specificity (86% compared with 84%), degree of coincidence of marked lesions (89% compared with 77%) and classification accuracy rate (96% compared with 88%).

authors

Hajimani E,Ruano MG,Ruano AE

doi

10.1016/j.cmpb.2017.05.005

subject

Has Abstract

pub_date

2017-07-01 00:00:00

pages

109-123

eissn

0169-2607

issn

1872-7565

pii

S0169-2607(16)30626-5

journal_volume

146

pub_type

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