MULTIWAVELET TRANSFORM BASED GLAUCOMA CLASSIFICATION USING RANDOM FOREST
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 9, Pages 1470-1475
AbstractGlaucoma is a group of eye disorders that damage the optic nerve, essential for good vision. Often this damage comes from an abnormally high pressure in the eye. The early diagnosis of glaucoma detection is required because it leads to loss of vision. The fundus images are decomposed by Multi Wavelet Transform (MWT). Then the sub-band coefficients of MWT are extracted by using energy features. Then the redundant features are reduced by Principal Component Analysis (PCA). Finally, Random Forest (RF) classifier is used for prediction. The classification results are obtained in the experimental results and discussion section. The system produces classification accuracy of 93% by using MWT based PCA reduction and RF classifier.
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