Automatic classification of dyslexic children by applying machine learning to fMRI images
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Fecha
2014-11-01
Autores
García Chimeno, Yolanda
García-Zapirain, Begoña
Saralegui Prieto, Ibone
Fernández-Ruanova, Begoña
Título de la revista
ISSN de la revista
Título del volumen
Editor
IOS Press
Resumen
Functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI) are a source of information to study different pathologies. This tool allows to classify subjects under study, analysing in this case, the functions related to language in young patients with dyslexia. Images are obtained using a scanner and different tests are performed on subjects. After processing the images, the areas that are activated by patients when performing the paradigms or anatomy of the tracts were obtained. The main objective is to ultimately introduce a group of monocular vision subjects, whose brain activation model is unknown. This classification helps to assess whether these subjects are more akin to dyslexic or control subjects. Machine learning techniques study systems that learn how to perform non-linear classifications through supervised or unsupervised training, or a combination of both. Once the machine has been set up, it is validated with the subjects who have not been entered in the training stage. The results are obtained using a user-friendly chart. Finally, a new tool for the classification of subjects with dyslexia and monocular vision was obtained (achieving a success rate of 94.8718% on the Neuronal Network classifier), which can be extended to other further classifications.
Palabras clave
Classifier
Dyslexic
FMRI
Monocular vision
PCA
Dyslexic
FMRI
Monocular vision
PCA
Descripción
Ponencia presentada en la 3rd International Conference on Biomedical Engineering and Biotechnology, celebrada en Beijing, China entre el 25 y el 28 de septiembre de 2014
Materias
Cita
Chimeno, Y. G., Zapirain, B. G., Prieto, I. S., & Fernandez-Ruanova, B. (2014). Automatic classification of dyslexic children by applying machine learning to fMRI images. Bio-Medical Materials and Engineering, 24(6), 2995-3002. https://doi.org/10.3233/BME-141120