Aprendizagem Profunda Aplicada ao Diagnóstico do Glaucoma

• Ano de defesa: 2019
• Autor(a) principal: LIMA, Alan Carlos de Moura
• Orientador(a): BRAZ JÚNIOR, Geraldo
• Banca de defesa: BRAZ JÚNIOR, Geraldo , ALMEIDA, João Dallyson Sousa de , PAIVA, Anselmo Cardoso de , VERAS, Rodrigo de Melo Souza
• 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: Diagnóstico de glaucoma; Aprendizagem profunda; Meta learning; Algoritmos genéticos
• Palavras-chave em Inglês: Glaucoma diagnosis; Deep learning; Meta learning; Genetic algorithms
• Área do conhecimento CNPq: Teoria da Computação; Análise de Algoritmos e Complexidade de Computação; Oftalmologia
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/2576

Glaucoma is a cluster of ocular diseases that cause damage to the eye’s optic nerve and cause successive narrowing of the visual field in affected patients, due to an increase in intraocular pressure, which can lead the patient to blindness at an advanced stage without clinical reversal. For several years, from techniques of manual analysis of the internal structures of the eye to the use of deep learning with convolutional neural networks (CNNs) were successfully used in the diagnosis of glaucoma. However, building a deep learning network requires a lot of effort that in many situations is not always able to achieve satisfactory results due to the amount of parameters that need to be configured to adapt the CNN architecture to the problem in question. The objective of this work is to use a hyperparameter search technic to select the tuned parameters of a genetic algorithm (GA) to select the best CNN architecture through evolutionary techniques and to be able to aid in the accurate diagnosis of glaucoma, in eye fund images. The proposed methodology was applied in 455 images from RIM-ONE dataset, in its version 2 (r2), with resized images to 96x96 pixels in the RGB color model. The selected CNN by AG, after its training, achieved for the diagnosis of glaucoma the results of 96.63% for accuracy, 94.87% for sensitivity, 98.00% for specificity, 97.37% for precision and 96.10% for f-score.