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Previous issue date: 2016-03-28 / O Espectro Autista (EA) compreende uma s?rie de desordens no desenvolvimento neurol?gico,
caracterizado por defici?ncias sociais e dificuldades de comunica??o, comportamentos repetitivos
e atrasos cognitivos. Atualmente, o diagn?stico do EA ? amplamente baseado em medi??es
comportamentais, que pode ser demorado, e depende da coopera??o do paciente e da experi?ncia
do examinador. Para mitigar esta limita??o, investigamos padr?es neurais que ajudem no diagn?stico
de desordens do EA. Nesta disserta??o, usamos t?cnicas de deep learning, a fim de extrair
caracter?sticas robustas de neuroimagens de pacientes com autismo. Neuroimagens cont?m cerca de
300.000 pontos espaciais, com aproximadamente 200 medi??es cada. As t?cnicas de deep learning
s?o ?teis para extrair caracter?sticas relevantes que diferenciam autistas de n?o-autistas. Ao utilizar
denoising autoencoders, uma t?cnica de deep learning espec?fica que visa reduzir a dimensionalidade
dos dados, n?s superamos o estado da arte, atingindo 69% de acur?cia, comparado com o melhor
resultado encontrado na literatura, com 60% de acur?cia. / Autism Spectrum Disorders (ASD) comprise a range of neurodevelopmental disorders,
characterized by social deficits and communication difficulties, repetitive behaviors, and cognitive
delays. The diagnosis of ASD is largely based on behavioral measurements, which can be timeconsuming
and relies on the patient cooperation and examiner expertise. In order to address this
limitation, we aim to investigate neural patterns to help in the diagnosis of ASD. In this dissertation,
we use deep learning techniques to extract robust characteristics from neuroimages of autistic subject
brain function. Since neuroimage contains about 300,000 spatial points, with approximately 200
temporal measurements each, deep learning techniques are useful in order to extract important
features to discriminate ASD subjects from non-ASD. By using denoising autoencoders, a specific
deep learning technique that aims to reduce data dimensionality, we surpass the state-of-the-art by
achieving 69% of accuracy, compared to 60% using the same dataset.
| Identifer | oai:union.ndltd.org:IBICT/oai:tede2.pucrs.br:tede/7459 |
| Date | 28 March 2016 |
| Creators | Heinsfeld, Anibal S?lon |
| Contributors | Meneguzzi, Felipe Rech, Franco, Alexandre Rosa |
| Publisher | Pontif?cia Universidade Cat?lica do Rio Grande do Sul, Programa de P?s-Gradua??o em Ci?ncia da Computa??o, PUCRS, Brasil, Faculdade de Inform?tica |
| Source Sets | IBICT Brazilian ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Source | reponame:Biblioteca Digital de Teses e Dissertações da PUC_RS, instname:Pontifícia Universidade Católica do Rio Grande do Sul, instacron:PUC_RS |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 1974996533081274470, 600, 600, 600, -3008542510401149144, 3671711205811204509 |
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