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Venugopal, K. R.
- Multistage Classification of Diabetic Retinopathy Using Fuzzyneural Network Classifier
Abstract Views :203 |
PDF Views:4
Authors
Affiliations
1 Department of Computer Science Engineering, B N M Institute of Technology, IN
2 Department of Computer Science Engineering, University Visvesvaraya College of Engineering, IN
1 Department of Computer Science Engineering, B N M Institute of Technology, IN
2 Department of Computer Science Engineering, University Visvesvaraya College of Engineering, IN
Source
ICTACT Journal on Image and Video Processing, Vol 8, No 4 (2018), Pagination: 1739-1746Abstract
Diabetic Retinopathy (DR) is complicated disorder in human retina which is affected due to an increasing amount of insulin in blood that results in vision impairment. Early detection of DR is used to support the patients to prevent blindness and to be aware of this disease. This paper proposes a novel technique for detecting DR using hybrid classifiers. It includes pre-processing of the image, segmentation of region of interest, feature extraction and classification. Retinal structures like microaneurysms, exudates, hemorrhages and blood vessels are segmented. Classification is performed with integration of Fuzzy logical System and Neural Network (NN) which improves the accuracy of classification. Experimentation is carried out with the MESSIDOR data set. Results are compared against various performance metrics like accuracy, sensitivity and specificity. An accuracy close to 100 percent and low average error rate of 0.012 are obtained using the proposed method. The results obtained are encouraging.Keywords
Diabetic Retinopathy, Hybrid Classifier, Visual Impairment, Fundus Images, Classification, Fuzzy Neural Network.
References
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- Improved Automatic Detection of Glaucoma using Cup-To-Disk Ratio and Hybrid Classifiers
Abstract Views :164 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science and Engineering, BNM Institute of Technology, IN
2 University Visvesvaraya College of Engineering, Bangalore University, IN
1 Department of Computer Science and Engineering, BNM Institute of Technology, IN
2 University Visvesvaraya College of Engineering, Bangalore University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 2 (2018), Pagination: 1901-1910Abstract
Glaucoma is one of the most complicated disorder in human eye that causes permanent vision loss gradually if not detect in early stage. It can damage the optic nerve without any symptoms and warnings. Different automated glaucoma detection systems were developed for analyzing glaucoma at early stage but lacked good accuracy of detection. This paper proposes a novel automated glaucoma detection system which effectively process with digital colour fundus images using hybrid classifiers. The proposed system concentrates on both Cup-to Disk Ratio (CDR) and different features to improve the accuracy of glaucoma. Morphological Hough Transform Algorithm (MHTA) is designed for optic disc segmentation. Intensity based elliptic curve method is used for separation of optic cup effectively. Further feature extraction and CDR value can be estimated. Finally, classification is performed with combination of Naive Bayes Classifier and K Nearest Neighbour (KNN). The proposed system is evaluated by using High Resolution Fundus (HRF) database which outperforms the earlier methods in literature in various performance metrics.Keywords
Glaucoma, Optic Nerve, Cup-To-Disc Ratio, HRF Database, Hybrid Classifier.References
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