Recognition and Classification of Diabetic Retinopathy utilizing Digital Fundus Image with Hybrid Algorithms
K. Malathi1, R. Kavitha2
1K. Malathi, Associate Professor, Computer Science and Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India.
2R. Kavitha, Assistant Professor (SG), Information Technology SRM IST, Ramapuram Chennai, India.
Manuscript received on September 14, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 109-122 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1058109119/2019©BEIESP | DOI: 10.35940/ijeat.A1058.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Diabetic Retinopathy (Damage in Retina) is the most common threatening diabetic eye disease and cause leading vision loss and blindness. A patient with the diabetic disease needs to experience occasional screening of eye. To analysis, ophthalmologists may utilize fundus or retinal pictures of the patient gained from advanced fundus camera. However, if the symptoms are identified earlier and proper treatment is provided through regular screening and monitoring, the blindness or vision loss can be avoided. The present study is intended on developing an automatic system for the analysis of the retinal of fundus images by using image-processing techniques. So as to accelerate the procedure, the discovery of diabetic retinopathy image processing methods is utilized In this proposed study, the performance is evaluated using different segmentation algorithms and classifiers namely fuzzy c-means clustering, naïve Bayesian classifier, support vector machine to detect the diabetic retinopathy. The presentation of the strategy is assessed on the freely accessible retinal databases like DRIVE, STARE. The presentation of the retinal vessels on DRIVE database, sensitivity 100% and specificity 97.5% while for STARE database the sensitivity 99%, specificity 97%.The detection of accuracy can be defined with respect to expert physician hand-drawn and ground truths and the results are comparatively obtained and analyzed.
Keywords: Diabetic Retinopathy, Fundus Image, Classifiers, Fuzzy c means, SVM, Medial Image.