An Automatic Classification of Glaucoma Disease using Knowledge Discovery Approach
M. Nageswara Rao1, M. Venu Gopala Rao2, Ch. K Priya3
1M. Nageswara Rao, Research Scholar, KL University, Asst. Professor, Department of Electronics and Communication Engineering, Sri Mittapalli College of Engineering, India.
2Dr. M. Venu Gopala Rao, Professor, Department of Electronics and Communication Engineering, KL University, India.
3Ch. K Priya, Asst. Professor, Department of Electronics and Communication Engineering, Narasaraopet Engineering College, India.
Manuscript received on 02 April 2019 | Revised Manuscript received on 10 May 2019 | Manuscript published on 30 May 2019 | PP: 1906-1910 | Volume-8 Issue-1, May 2019 | Retrieval Number: A1906058119/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Glaucoma is one of the dangerous disease which consequences in the loss of vision of the individual. The main cause of this type of disease is the hypertension or any communicable disease which deals with high disorders in disturbing the optic nerves in the retina of the individual. This paper deals with the efficient learning approach for the automatic classification of the glaucoma disease which deals with the less error rate probabilities and having high recognition rate. This paper deals with the segmentation and filtration using discrete wavelet transform and feature extraction and optimization on the basis of which the classification will be done using linear discriminant analysis. The feature extraction is done using independent component analysis and the feature optimization is done using particle swarm optimization. The whole simulation is done in MATLAB environment.
Index Terms: Automatic Classification, Discriminant Analysis, Recognition Rate, Error Rate Probabilities
Scope of the Article: Classification