RESUMEN
The paper outlines an integrated image processing environment that uses neural networks ActiveX technology for object recognition and classification. The image processing environment which is Windows based, encapsulates a Multiple-Document Interface (MDI) and is menu driven. Object (shape) parameter extraction is focused on features that are invariant in terms of translation, rotation and scale transformations. The neural network models that can be incorporated as ActiveX components into the environment allow both clustering and classification of objects from the analysed image. Mapping neural networks perform an input sensitivity analysis on the extracted feature measurements and thus facilitate the removal of irrelevant features and improvements in the degree of generalisation. The program has been used to evaluate the dimensions of the hydrocephalus in a study for calculating the Evans index and the angle of the frontal horns of the ventricular system modifications.
Asunto(s)
Procesamiento de Imagen Asistido por Computador , Sistemas Integrados y Avanzados de Gestión de la Información , Redes Neurales de la Computación , Humanos , Programas InformáticosAsunto(s)
Enfermedades de los Genitales Femeninos/diagnóstico , Infecciones Estreptocócicas/diagnóstico , Streptococcus agalactiae , Adolescente , Adulto , Técnicas Bacteriológicas/estadística & datos numéricos , Niño , Femenino , Genitales Femeninos/metabolismo , Genitales Femeninos/microbiología , Humanos , Pruebas de Sensibilidad Microbiana , Persona de Mediana Edad , Streptococcus agalactiae/efectos de los fármacos , Streptococcus agalactiae/aislamiento & purificaciónAsunto(s)
Infecciones Estreptocócicas/microbiología , Streptococcus/patogenicidad , Antibacterianos/farmacología , Relación Dosis-Respuesta a Droga , Enterococcus/clasificación , Enterococcus/efectos de los fármacos , Enterococcus/aislamiento & purificación , Humanos , Pruebas de Sensibilidad Microbiana/estadística & datos numéricos , Serotipificación , Streptococcus/clasificación , Streptococcus/efectos de los fármacos , Streptococcus/aislamiento & purificación , Streptococcus agalactiae/clasificación , Streptococcus agalactiae/efectos de los fármacos , Streptococcus agalactiae/aislamiento & purificación , Streptococcus pyogenes/clasificación , Streptococcus pyogenes/efectos de los fármacos , Streptococcus pyogenes/aislamiento & purificaciónRESUMEN
Evolutionary artificial neural networks (EANN) are a new paradigm that refers to a special class of artificial neural networks (ANN) in which evolution is another fundamental form of adaptation in addition to learning. Evolution can be introduced at various levels of ANN. It can be used to evolve weights, architectures and learning parameters. Evolutionary computations are population-based search methods that have shown promise in many similarly complex tasks. This paper presents an application of evolutionary programming for simultaneously inducing the input structure and weights evolving for multilayer feed-forward perceptrons (MLP) with standard sigmoidal activation function.