Authors: M. Kavitha; S. Palani
Addresses: Department of Electronics and Communication Engineering, K. Ramakrishnan College of Engineering and Technology, Trichy, India ' Department of ECE, Sudharsan Engineering College, Pudukkottai (d.t), Tamil Nadu, India
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.
International Journal of Business Information Systems, 2020 Vol.34 No.2, pp.229 - 252
Received: 12 Mar 2018
Accepted: 24 May 2018
Published online: 10 Jul 2020 *