An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images.
Comput Methods Programs Biomed
; 146: 109-123, 2017 Jul.
Article
em En
| MEDLINE
| ID: mdl-28688480
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%).Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Encéfalo
/
Tomografia Computadorizada por Raios X
/
Redes Neurais de Computação
/
Acidente Vascular Cerebral
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article