Abstract
This paper shows the performance for diagnosis of fundus images using the subspace method. Feature extraction was conducted with three kinds of image (R, G and B pixel), and the feature vector and the subspace dimension for recognition were determined. Afterward, a series of analyses on the accuracy were conducted. The recognition accuracy of the fundus image by using the subspace method was compared with the results obtained by two methods: Learning Vector Quantization and Multi-Layer Perceptron. In the experiments, consequently, a maximum accuracy rate of 75.2% was obtained by using the subspace method, in which the accuracy was the highest performance among three methods' accuracy.
