Classifica??o de lateralidade hemisf?rica de linguagem de linguagem em imagens de resson?ncia magn?tica funcional utilizando aprendizado de m?quina

• Ano de defesa: 2020
• Autor(a) principal: Morais, Mariana Pastro
• Orientador(a): Silva, Ana Maria Marques da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Pontif?cia Universidade Cat?lica do Rio Grande do Sul
• Programa de Pós-Graduação: Programa de P?s-Gradua??o em Engenharia El?trica
• Departamento: Escola Polit?cnica
• País: Brasil
• Palavras-chave em Português: Resson?ncia Magn?tica Funcional; Aprendizado de M?quina; Neurolinguagem; Lateralidade Hemisf?rica Cerebral
• Palavras-chave em Inglês: Functional Magnetic; Resonance; Machine Learning; Neurolanguage; Cerebral Hemispheric Laterality
• Área do conhecimento CNPq: ENGENHARIAS
• Link de acesso: http://tede2.pucrs.br/tede2/handle/tede/9334

Although the cerebral language area has its anatomical location defined by Wernicke?s area and Broca?s area, some patients affected with brain tumors have their activation topographies altered due to a phenomenon called language translocation. Functional Magnetic Resonance Imaging (fMRI) is a diagnostic imaging method that highlights language areas, contributing to the correct surgical planning of tumor removal. Despite the detection of brain activation areas by visual inspection being the most used at the clinic, some studies have been using Machine Learning techniques, such as Support Vector Machine, Logistic Regression, Decision Trees, and others. This work aims to evaluate the application of a technique for classification language area laterality to be using Machine Learning. The activation coefficients of the areas of interest in fMRI images were extracted using the AFNI software. A Multilayer Perceptron artificial neural network was set up in order to determine the probability of the language area are allocated in a certain cerebral hemisphere. The performance of the neural network was assessed by means of statistical comparison with the Laterality Index and the visual analisys. The results showed that MLP RNA has the potential to be an auxiliary method for classifying cerebral hemispheric laterality in the language area.