 A comprehensive analysis for retinal image classification methods using real-time databaseM. Kavitha and S. Palani International Journal of Business Information Systems, 2020, vol. 34, issue 2, 229-252 Abstract: This paper proposes a comprehensive analysis to compare our two methods with some existing methods to prove the improvements of the proposed algorithm. Here, the comparative analysis is done by three phases like pre-processing, segmentation and classifier phase. For pre-processing, four noise removal filters like average, Laplacian, motion and unsharp are carried out to compare with Gaussian filter. The two segmentation algorithms like region growing and k-means segmentation are carried out to compare in proposed segmentation phase. For diabetic classification, the proposed classifier of Levenberg-Marquardt (LM) neural network against other four existing classifiers SCG-NN, adaptive neuro-fuzzy inference system and k-NN. Here, some different evaluation metrics such as PSNR, SSIM, sensitivity, specificity and accuracy are used to measure the performance. The test images are considered for performance analysis in real-time. The proposed approach obtained 97.5% in terms of accuracy for real-time database, which is high compared to existing techniques. Keywords: abnormal; normal; hard; soft; diabetic retinopathy; DR; classifier; retinal image. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:34:y:2020:i:2:p:229-252 Access Statistics for this article More articles in International Journal of Business Information Systems from Inderscience Enterprises Ltd |
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