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- An intelligent support system for automatic detection of cerebral vascular accidents from brain CT imagesPublication . Hajimani, Elmira; Ruano, M G; Ruano, AntonioObjective: 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%). (C) 2017 Published by Elsevier Ireland Ltd.
- A radial basis function classifier for the automatic diagnosis of cerebral vascular accidentsPublication . Ruano, M. Graça; Hajimani, Elmira; Ruano, AntonioA Radial Basis Function Neural Network (RBFNN) based diagnosis system for automatic identification of Cerebral Vascular Accident (CVA) through analysis of Computer Tomographic images (CT) is presented. For the design of a neural network classifier, most published methods just focus on the feature selection aspect and do not consider any approach for determining a model structure that best fits the application at their hand. Moreover, considering the domain of lesion detection from brain tissues, their feature space rarely contains symmetry/asymmetry information with respect to ideal mid-sagittal line. Another issue is how to handle multiple conflicting objectives in the design process, such as the maximization of both specificity and sensitivity, enforcing as well generalization. To deal with these challenges, a Multi Objective Genetic Algorithm (MOGA) based approach is used to determine the architecture of the classifier, its corresponding parameters and input features subject to multiple objectives, as well as their corresponding restrictions and priorities.
- A software tool for intelligent CVA diagnosis by cerebral computerized tomographyPublication . Hajimani, Elmira; Ruano, Carina A.; Ruano, M. Graça; Ruano, AntonioThe final goal of this work is to create an intelligent support system which assists neuroradiologists to identify Cerebral Vascular Accidents in less time, more precisely. For this purpose, the first step was the creation of a web based tool for registering pathological areas in CT images, which will allow to collect required data for training and testing our proposed classifier, a Radial Basis Function (RBF) based Neural Network.