Métodos de Classificação de Glaucoma em Imagens do Disco Óptico

Título: Métodos de Classificação de Glaucoma em Imagens do Disco Óptico

Autores: Débora Ferreira De Assis, Paulo César Cortez, Pedro Motta

Resumo: Glaucoma is an asymptomatic eye disease that, if not treated, can lead to blindness. The World Health Organization (WHO) estimated that by 2020 glaucoma should affect 80 million people and by 2040 will be 111.5 million. In this context, the present work aims to compare automatic classification methods to assist the specialist physician in the diagnosis of glaucoma. For this purpose, a model based on the extraction of non-geometric characteristics of optical disk images from the RIM-ONE r2 dataset had been developed. These characteristics were submitted to Principal Component Analysis (PCA) for dimensionality reduction, the resulting components served as input to the classifiers: Logistic Regression (RL), Decision Tree Gradient Boosting (DTGB), Support Vector Machine ( SVM), k nearest neighbors (k-NN) and Multilayer Perceptron (MLP). To evaluate the results we used the accuracy, sensitivity, specificity, positive predictive value, negative predictive value and area under curve (AUC). The results demonstrate good performance with all classifiers, especially MLP, the test results reached an accuracy of 97.83 %, sensitivity 100 %, specificity 96.15 %, vpp 95.24 %, vpn 100 % and auc 97.62 %.

Palavras-chave: Glaucoma; Classification; MLP; PCA

Páginas: 8

Código DOI: 10.21528/CBIC2019-95

Artigo em pdf: CBIC2019-95.pdf

Arquivo BibTeX: CBIC2019-95.bib