Classification of Fundus Images for Diagnosing Glaucoma by Self-Organizing Map and Learning Vector Quantization

摘要

This paper presents a two stage diagnosis system that consists of Self-Organizing Map (SOM) and Learning Vector Quantization (LVQ) subsystems for diagnosis of fundus images. The first stage performs clustering and pseudo-classification of the input feature data by a SOM. The use of the pseudo-classes is able to improve the performance of the second stage consisting of a LVQ codebook. The proposed system has been tested on real medical treatment image data. In the experiments we have achieved a maximum accuracy rate of 71.2%, which is comparable to other results in literature.