Automated Detection of Diabetic Retinopathy for Early Diagnosis using Exudate Images
P. Manimegalai1, S. Soundarya2, J. R. Aswath3, M. Sowmiya4, N. Raja Lakshmi5
1P. Manimegalai, Associate Professor, Department of Instrumentation Engineering, Karunya Institute of Tech and Sciences, Coimbatore (Tamil Nadu), India.
2S. Soundarya, UG Scholar, Department of ECE, ECEKAHE Coimbatore (Tamil Nadu), India.
3J. R. Aswath, UG Scholar, Department of ECE, ECEKAHE, Coimbatore (Tamil Nadu), India.
4M. Sowmiya, UG Scholar, Department of ECE, ECEKAHE, Coimbatore (Tamil Nadu), India.
5N. Raja Lakshmi, Associate Professor, Department of Biomedical Engineering, KAHE, Coimbatore (Tamil Nadu), India.
Manuscript received on 30 September 2019 | Revised Manuscript received on 12 November 2019 | Manuscript Published on 22 November 2019 | PP: 1572-1576 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F12890986S319/19©BEIESP | DOI: 10.35940/ijeat.F1289.0986S319
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Abstract: Retina plays a vital character in detection of various diseases in early point such as diabetes retinopathy which can be performed by analyzing the retinal images [6]. Diseased patients have to undergo periodic screening of eye. Standouts amongst the most predominant clinical indications of diabetic retinopathy are exudates [17]. To detect diabetic retinopathy in patients the ophthalmologist inspects the exudates by Ophthalmoscopy [17] where recognition of exudates is a vital diagnostic undertaking in which computer help may assume a noteworthy job. But intrinsic characteristics of retinal images detection process is difficult for the ophthalmologists. Here, we proposed another algorithm “Superpixel Multi-Feature Classification” for the programmed automatic recognition of retinal exudates successfully and to encourage ophthalmologist to give better patient finding experiencing diabetic retinopathy, advising them the level of seriousness ahead of time. The performance of algorithm has been compared as a result, the outcomes are effective and the sensitivity and specificity for our exudates identification is 80% and 91.28%, respectively [15].
Keywords: Super Pixel, Diabetic Retinopathy, Exudates, Image Processing.
Scope of the Article: Image Security