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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Matsuda, N., Laaksonen, J., Tajima, F., & Sato, H. (2009). Classification of fundus images for diagnosing glaucoma by self-organizing map and learning vector quantization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5507 LNCS, pp. 703–710). https://doi.org/10.1007/978-3-642-03040-6_86
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