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Diabetic Retinopathy (DR) is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. Ophthalmologists recognize diabetic retinopathy based on features, such as blood vessel area, exudes, hemorrhages, microaneurysms and texture.
Aim: The focus of this paper is to evaluate the performance of Decision Tree (DT), Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) Classifiers in Diabetes Retinopathy Detection.
Results: Corresponding results showed SVM has the best classification strength by achieving Recognition Accuracy (RA) of 98.50%, while PNN and DT achieved RA of 97.60% and 89.20% respectively. In terms of False Acceptance Rate (FAR) and False Rejection Rate (FRR), SVM has the least values of 7.21, 8.10 while DT and PNN showed 11.10, 9.30 and 13.21, 10.10 respectively. However, in this paper a Mobile based Diabetes Retinopathy Detection System was developed to make the system available for the masses for early detection of the disease.
Jayanthi D, Devi N, Swarna Parvathi S. Automatic diagnosis of retinal diseases from color retinal images. Int. J. Comput. Sci. Inf. Secur. 2010;7(1):234–238.
Giancardo L, Meriaudeau F, Karnowski T, Li Y, Tobin K, Chaum E. Micro aneurysm detection with radon transform-based classification on retina images. In Proc. Intl. Conf. IEEE Eng. Med. Biol. Soc. 2011;5939–5942.
Antal B, Lazar I, Hajdu A, Torok Z, Csutak A, Peto T. Evaluation of the grading performance of an ensemble-based microaneurysm detector. In Proc. Intl. Conf. IEEE Eng. Med. Biol. Soc. 2011;5943–5946.
Fleming AD, Philip S, Goatman KA, Olson JA, Sharp PF. Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans. Med. Image. 2006;25(9):1223–1232.
Cree MJ, Gamble E, Cornforth DJ. Colour normalisation to reduce inter-patient and intra-patient variability in microaneurysm detection in colour retinal images. In Proc. Workshop Digital Image Computer. 2005;163–168.
Pires RH, Jelinek F, Wainer J, Rocha A. Retinal image quality analysis for automatic diabetic retinopathy detection. In SIBGRAPI. 2012;1-8.
Osareh M, Mirmehdi B. Thomas, Markham R. Automated identification of diabetic retinal exudates in digital color images. British Journal of Ophthalmology. 2003;87(10).
Kullayamma I, Madhavee Latha P. Retinal image analysis for exudates detection. International Journal of Engineering Research and Applications (IJERA). 2013;3(1):1871-1875.
Hunter A, Lowell J, Owens J, Kennedy L. Quantification of diabetic retinopathy using neural networks and sensitivity analysis. In Proceedings of Artificial Neural Networks in Medicine and Biology. 2000;81-86.
Li H, hutatape O. Fundus image feature extraction. Proceedings 22nd Annual EMBS International Conference, Chicago. 2000;3071-3073.
Wang H, Hsu W, Goh KG, Lee M. An effective approach to detect lesions in colour retinal images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2000;181-187.
Zhang X, Chutatape O. Detection and classification of bright lesions in colour fundus images. Int. Conf. on Image Processing. 2004;1:139–142.
Alzubi J, Nayyar A, Kumar A. Machine learning from theory to algorithms: An overview. In Journal of Physics: Conference Series. IOP Publishing. 2018;1142(1):012012.
Xu M, Mandal R, Long I, Cheng, Basu A. An edge-region force guided active shape approach for automatic lung field detection in chest radiographs. Comput. Med. Imaging Graph. 2012;36(6):452–63.
Bay H, Tuytelaars T, Gool LV. SURF: Speeded up robust features. In ECCV. 2006;404-417.
Rasoul Safavian, David Landgrebe. Safavian SR, Landgrebe D. A survey of decision tree classifier methodology. IEEE Trans. Syst. Man. Cybern. 1991;21(3): 660–674.
Buddhiraju KM, Rizvi IA. Comparison of CBF, ANN and SVM classifiers for object-based classification of high resolution satellite images. In 2010 IEEE International Geoscience and Remote Sensing Symposium. 2010;40–43.
Radha R, Bijee Lakshman. Retinal image analysis using morphological process and clustering technique. Signal and Image Processing International Journal (SIPIJ). 2013;4(6):55-68.