Exploring Artificial Intelligence methods for the automatic measurement of a new biomarker aiming at glaucoma diagnosis
| Orientador(a) (dc.contributor.advisor) | Rigo, Sandro José | |
| Coorientador(a) (dc.contributor.advisor-co1) | Silva, Luiz José Schirmer | |
| Lattes Coorientador(a) (dc.contributor.advisor-co1Lattes) | http://lattes.cnpq.br/8490105798416004 | pt_BR |
| Lattes Orientador(a) (dc.contributor.advisorLattes) | http://lattes.cnpq.br/3914159735707328 | pt_BR |
| Autor(a) (dc.contributor.author) | Fernandes, Gabriel Castro | |
| Autor(a) Lattes (dc.contributor.authorLattes) | http://lattes.cnpq.br/2370330768411535 | pt_BR |
| Data de Disponibilização (dc.date.accessioned) | 2025-03-19T19:31:33Z | |
| dc.date.available (dc.date.available) | 2025-03-19T19:31:33Z | |
| Data da defesa / Data do evento (dc.date.issued) | 2024-07-26 | |
| Resumo (dc.description.resumo) | Analyzing retina structure in high-resolution images, such as those obtained in optical coherence tomography, is one of the most widespread ways of identifying structural changes that may indicate the onset or progression of visual impairment. During the diagnosis process, the specialist performs several manual analyses of the data generated by imaging equipment. There is a consensus regarding the benefits of having support from automated approaches to help in this diagnosis process. Nevertheless, automated glaucoma detection using optical coherence tomography is still considered an area needing further research. This work presents an approach to foster automatic glaucoma evaluation considering convolutional neural networks for semantic segmentation of retinal layers through optical coherence tomography images and image processing for measuring the cup region in the optic nerve head portion. We provide a quantitative evaluation comparing the results obtained by a specialist physician. The work’s main contribution is presenting the first approach supporting the automation of a new biomarker for diagnosing glaucoma. | pt_BR |
| Agência de fomento (dc.description.sponsorship) | Nenhuma | pt_BR |
| URI (dc.identifier.uri) | http://repositorio.jesuita.org.br/handle/UNISINOS/13562 | |
| Idioma (dc.language) | en | en |
| Nome da instituição (dc.publisher) | Universidade do Vale do Rio dos Sinos | pt_BR |
| País da Instituição (dc.publisher.country) | Brasil | pt_BR |
| Departamento (dc.publisher.department) | Escola Politécnica | pt_BR |
| Sigla da Instituição (dc.publisher.initials) | Unisinos | pt_BR |
| Programa (dc.publisher.program) | Programa de Pós-Graduação em Computação Aplicada | pt_BR |
| Direitos de acesso ao documento (dc.rights) | openAccess | pt_BR |
| Assunto (dc.subject) | Glaucoma | en |
| Assunto (dc.subject) | Artificial Intelligence | en |
| Assunto (dc.subject) | Diagnosis | en |
| Assunto (dc.subject) | Biomarker | en |
| Tema (CNPq) (dc.subject.cnpq) | ACCNPQ::Ciências Exatas e da Terra::Ciência da Computação | pt_BR |
| Título (dc.title) | Exploring Artificial Intelligence methods for the automatic measurement of a new biomarker aiming at glaucoma diagnosis | pt_BR |
| Tipo de arquivo (dc.type) | Dissertação | pt_BR |
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