Redes neurais multinível para classificação do ângulo da câmara anterior utilizando Imagens OCT-SA

• Ano de defesa: 2021
• Autor(a) principal: FERREIRA, Marcos Melo
• Orientador(a): BRAZ JUNIOR, Geraldo
• Banca de defesa: BRAZ JUNIOR, Geraldo , PAIVA, Anselmo Cardoso de , ALMEIDA, João Dallyson Sousa de , ARAÚJO, Sidnei Alves de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO/CCET
• Departamento: DEPARTAMENTO DE INFORMÁTICA/CCET
• País: Brasil
• Palavras-chave em Português: Glaucoma; Redes neurais convolucionais; Arquitetura multinível; Transferência de aprendizado
• Palavras-chave em Inglês: Glaucoma; Convolutional neural networks; Multilevel architecture; Transfer learning
• Área do conhecimento CNPq: Ciência da Computação
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/tede/3356

Glaucoma is identified as one of the main causes of visual impairment, and the main cause of irreversible blindness. The main forms of the disease are primary open-angle glaucoma and primary angle-closure glaucoma. In people with angle-closure glaucoma, the anterior chamber angle narrows, consequently causing an increase in intraocular pressure causing damage to the optic nerve, causing partial or total vision loss. As the damage is irreversible, an early diagnosis is essential, but it is hampered due to the fact that the disease is asymptomatic in early stages. For early detection of the disease, routine imaging tests are recommended, one of which is Anterior Segment Optical Coherence Tomography, which allows an angle classification, which is essential for diagnosis. An analysis of this type of image requires a degree of interpretation on the part of specialists, because of this, the evaluation of many images requires a lot of time, which can lead to professional fatigue. The use of automated methods to assist in the interpretation of images would contribute to get diagnoses more quickly. In this work, an automated method is proposed to classify the anterior chamber angle, present in Anterior Segment images, based on deep learning, using convolutional neural networks. Initially, five pre-trained models of convolutional networks were adjusted to perform feature extraction and classify images. Next, the models were combined in a multilevel architecture, with the objective of increasing the classification capacity. As best result achieved an AUC value (Area Under the Curve) of 0.999.