Abstract
Diabetic Retinopathy is the name given to ‘disease of retina’. The objective of this work is for timely diagnosis and classification of diabetic retinopathy using curvelet transforms and SVM. Firstly, retinal images are enhanced using empirical transform. Canny edge detection is applied for extracting eyeball from retinal fundus image. Then morphological operations are applied for locating the imperfections in the images. At the end, images are classified into normal, proliferative or non-proliferative by using SVM. Both accuracy and sensitivity of the images is improved when compared with previous technique in which only k-means and fuzzy classifier is used. The number of exudates detected in present work is more than that of the process without enhancement. The sensitivity, specificity and accuracy of system are calculated as 96.77, 100 and 97.78 respectively.
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References
Chauhan, R., Uniyal, A., Dubey, V.P.: Detection of retinal blood vessels and reduction of false microaneurysms for diagnosis of diabetic retinopathy. IEEE Trans. Med. Imaging 30, 191–196 (2016)
World Health Organization.: Global health estimates: deaths by cause, age, sex and country 2000–2012. In: Department of Health Statistics and Information Systems WHO, Geneva (2014)
Rakshitha, T.R., Devaraj, D., Prasanna, K.S.C.: Comparative study of imaging transforms on diabetic retinopathy images. In: IEEE International Conference on Recent Trends in Electronics Information Communication Technology, pp. 118–122, India (2016)
Manjula, R., Rajesh, V.: Early detection of diabetic retinopathy from retinal fundus images using Eigen value analysis. In: International Conference on Control, Instrumentation, Communication and Computational Technologies, vol. 1, Issue No. 15, pp. 347–351 (2015)
Jahiruzzaman, M.D., Hossain, A.: Detection and classification of diabetic retinopathy using K-Means clustering and fuzzy logic. In 18th International Conference on Computing And Information Technology, pp. 534–538 (2015)
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Kaur, S., Singh, D. (2018). Early Detection and Classification of Diabetic Retinopathy Using Empirical Transform and SVM. In: Hemanth, D., Smys, S. (eds) Computational Vision and Bio Inspired Computing . Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-71767-8_92
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DOI: https://doi.org/10.1007/978-3-319-71767-8_92
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