An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images. (July 2017)

• Record Type: Journal Article
• Authors: Hajimani, Elmira; Ruano, M.G.; Ruano, A.E.
• Is Part Of: Computer methods and programs in biomedicine. Volume 146(2017)
• Page Start: 109
• Page End: 123
• Publication Date: 2017-07
• Subjects: Neural networks -- Symmetry features -- Multi-objective genetic algorithm -- Intelligent support systems -- Cerebral vascular Accident; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.05.005
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A RBFNN based system for automatic diagnosis of CVAs from brain CT is proposed. The best possible RBFNN topology, inputs and parameters are identified by MOGA. Symmetry features along with 1st and 2nd order statistics compose the feature space. 98.01% specificity and 98.22% sensitivity in a set of 1867, 602 pixels are achieved. The proposed approach compares favourably with existing approaches. 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, 602 pixels), 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%).