Abstract
This paper shows the performance of glaucoma diagnosis of fundus images using the Subspace Classifier method. Feature extraction is based on three image color channels (R, G and B), and the optimal subspace dimensionality for the analysis of the fundus has been selected. A series of the analyses has been conducted, in which the classification accuracy for glaucoma in the fundus image has been compared between the proposed method and two other methods, namely the Learning Vector Quantization and Multi-Layer Perceptrons. In the experiments a maximum recognition rate of 75.3% was obtained by using the Subspace Classifier method, which was the best-performing one among the three methods compared.
