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Currently, the diagnosis of Alzheimer's disease is a complex and error-proneprocess. Improving this diagnosis could allow earlier detection of the diseaseand improve the quality of life of patients and their families. For this work,we will use 249 brain images from two modalities: PET and MRI, taken from theADNI database, and labelled into three classes according to the degree ofdevelopment of Alzheimer's disease. We propose the development of aconvolutional neural network to perform the classification of these images,during which, we will study the appropriate depth of the networks for thisproblem, the importance of pre-processing medical images, the use of transferlearning and data augmentation techniques as tools to reduce the effects of theproblem of having too little data, and the simultaneous use of multiple medicalimaging modalities. We also propose the application of an evaluation methodthat guarantees a good degree of repeatability of the results even when using asmall dataset. Following this evaluation method, our best final model, whichmakes use of transfer learning with COVID-19 data, achieves an accuracy d 68\%.In addition, in an independent test set, this same model achieves 70\%accuracy, a promising result given the small size of our dataset. We furtherconclude that augmenting the depth of the networks helps with this problem,that image pre-processing is a fundamental process to address this type ofmedical problem, and that the use of data augmentation and the use ofpre-trained networks with images of other diseases can provide significantimprovements.

Aplicaci\'on de redes neuronales convolucionales profundas al diagn\'ostico asistido de la enfermedad de Alzheimer