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Convolutional Neural Network for the Classification of Independent Components of rs-fMRI

Redes neuronales convolucionales para la clasificación de componentes independientes de rs-fMRI

dc.creatorMera-Jiménez, Leonel
dc.creatorOchoa-Gómez, John F.
dc.date2021-01-30
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1626
dc.identifier10.22430/22565337.1626
dc.descriptionResting state functional magnetic resonance imaging (rs-fMRI) is one of the most relevant techniques in brain exploration. However, it is susceptible to many external factors that can occlude the signal of interest. In this order of ideas, rs-fMRI images have been studied adopting different approaches, with a particular interest in artifact removal techniques through Independent Component Analysis (ICA). Such an approach is a powerful tool for blind source separation, where elements associated with noise can be eliminated. Nevertheless, such removal is subject to the identification or classification of the components provided by the ICA. In that sense, this study focuses on finding an alternative strategy to classify the independent components. The problem was addressed in two stages. In the first one, the components (3D volumes) were reduced to images by Principal Component Analysis (PCA) and by obtaining the median planes. The methods achieved a reduction of up to two orders of magnitude in the weight of the data size, and they were shown to preserve the spatial characteristics of the independent components. In the second stage, the reductions were used to train six models of convolutional neural networks. The networks analyzed in this study reached accuracies around 98 % in classification, one of them even up to 98.82 %, which reflects the high discrimination capacity of convolutional neural networks.en-US
dc.descriptionLa resonancia magnética funcional en estado de reposo (rs-fMRI) es una de las técnicas más relevantes en exploración cerebral. No obstante, la misma es susceptible a muchos factores externos que pueden ocluir la señal de interés. En este orden de ideas, las imágenes rs-fMRI han sido estudiadas desde diferentes enfoques, existiendo un especial interés en las técnicas de eliminación de artefactos a través del Análisis de Componentes Independientes (ICA por sus siglas en inglés). El enfoque es una herramienta poderosa para la separación ciega de fuentes donde es posible eliminar los elementos asociados a ruido. Sin embargo, dicha eliminación está sujeta a la identificación o clasificación de las componentes entregadas por ICA. En ese sentido, esta investigación se centró en encontrar una estrategia alternativa para la clasificación de las componentes independientes. El problema se abordó en dos etapas. En la primera de ellas, se redujeron las componentes (volúmenes 3D) a imágenes mediante el Análisis de Componentes Principales (PCA por sus siglas en inglés) y con la obtención de los planos medios. Los métodos lograron una reducción de hasta dos órdenes de magnitud en peso de los datos y, además, demostraron conservar las características espaciales de las componentes independientes. En la segunda etapa, se usaron las reducciones para entrenar seis modelos de redes neuronales convolucionales. Las redes analizadas alcanzaron precisiones alrededor de 98 % en la clasificación e incluso se encontró una red con una precisión del 98.82 %, lo cual refleja la alta capacidad de discriminación de las redes neuronales convolucionales.es-ES
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dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1626/1813
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1626/1819
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1626/1827
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1626/1926
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dc.rightsCopyright (c) 2020 TecnoLógicasen-US
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0en-US
dc.sourceTecnoLógicas; Vol. 24 No. 50 (2021); e1626en-US
dc.sourceTecnoLógicas; Vol. 24 Núm. 50 (2021); e1626es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectIndependent Component Analysisen-US
dc.subjectPrincipal Component Analysisen-US
dc.subjectConvolutional Neural Networken-US
dc.subjectdenoising in fMRIen-US
dc.subjectresting-stateen-US
dc.subjectAnálisis de Componentes Independienteses-ES
dc.subjectAnálisis de Componentes Principaleses-ES
dc.subjectRedes Neuronales Convolucionaleses-ES
dc.subjectreducción de ruido en fMRIes-ES
dc.subjectestado de reposoes-ES
dc.titleConvolutional Neural Network for the Classification of Independent Components of rs-fMRIen-US
dc.titleRedes neuronales convolucionales para la clasificación de componentes independientes de rs-fMRIes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES


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