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In this work, deep learning techniques for brain age prediction from magneticresonance images are investigated, aiming to assist in the identification ofbiomarkers of the natural aging process. The identification of biomarkers isuseful for detecting an early-stage neurodegenerative process, as well as forpredicting age-related or non-age-related cognitive decline. Two techniques areimplemented and compared in this work: a 3D Convolutional Neural Networkapplied to the volumetric image and a 2D Convolutional Neural Network appliedto slices from the axial plane, with subsequent fusion of individualpredictions. The best result was obtained by the 2D model, which achieved amean absolute error of 3.83 years.  --  Neste trabalho s\~ao investigadas t\'ecnicas de aprendizado profundo para apredi\c{c}\~ao da idade cerebral a partir de imagens de resson\^anciamagn\'etica, visando auxiliar na identifica\c{c}\~ao de biomarcadores doprocesso natural de envelhecimento. A identifica\c{c}\~ao de biomarcadores \'e\'util para a detec\c{c}\~ao de um processo neurodegenerativo em est\'agioinicial, al\'em de possibilitar prever um decl\'inio cognitivo relacionado oun\~ao \`a idade. Duas t\'ecnicas s\~ao implementadas e comparadas nestetrabalho: uma Rede Neural Convolucional 3D aplicada na imagem volum\'etrica euma Rede Neural Convolucional 2D aplicada a fatias do plano axial, composterior fus\~ao das predi\c{c}\~oes individuais. O melhor resultado foiobtido pelo modelo 2D, que alcan\c{c}ou um erro m\'edio absoluto de 3.83 anos.

Predi\c{c}\~ao da Idade Cerebral a partir de Imagens de Resson\^ancia Magn\'etica utilizando Redes Neurais Convolucionais