Diagnosis of Retinal Disease using ANFIS Classifier

Corneal images can be acquired using confocal microscopes which provide detailed views of the different layers inside a human cornea. Some corneal problems and diseases can occur in one or more of the main corneal layers: the epithelium, stoma and endothelium. In this method, we have proposed a novel framework for automatic detection of true retinal area in SLO images. Artificial neural networks (ANNs) and, adaptive neuro fuzzy inference systems (ANFIS) have been investigated and tested to improve the recognition accuracy of the main corneal layers and identify abnormality in these layers. Feature selection is necessary so as to reduce computational time during training and classification. It shows that selection of features based on their mutual interaction can provide the classification power close to that of feature set with all features. As far as the classifier is concerned, the testing time of ANFIS was the lowest compared to other classifiers. The performance of the ANFIS achieves an accuracy of 100% for some classes in the processed data sets. This system is able to pre-process (quality enhancement, noise removal), classify corneal images, and identify abnormalities in the analyzed data sets. Citation: Dr.A.Umarani, K.L.N. College of Engineering; V.Madhura veena ,; K.Dhivya Bharathi ,. "Diagnosis of Retinal Disease using ANFIS Classifier." Global Research and Development Journal For Engineering : 324 - 332.