Diabetic Maculopathy Detection using Image Processing
J. Pradeep Kandhasamy1, S. Balamurali2, M. Arun3, M. Mariappan4
1J. Pradeep Kandhasamy, Department of Computer Applications, School of Computing, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
2S. Balamurali, Department of Computer Applications, School of Computing, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
3M. Arun, Department of Computer Applications, School of Computing, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
4M. Mariappan, Department of Computer Applications, School of Computing, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
Manuscript received on 23 November 2019 | Revised Manuscript received on 17 December 2019 | Manuscript Published on 30 December 2019 | PP: 115-119 | Volume-9 Issue-1S4 December 2019 | Retrieval Number: A11021291S419/19©BEIESP | DOI: 10.35940/ijeat.A1102.1291S419
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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 (DR) is a serious eye disease caused to human beings having diabetics. DR will affect the retina of the eye and even it may lead to complete blindness. It is essential to have an early treatment for the diagnosis of DR to avoid blindness. There are many physical tests like visual test, pupil dilation to detect retinopathy but all are time consuming processes. For diabetic retinopathy, it needs a continuous monitoring process. The main objective of this work is to detect diabetic maculopathy which is one of the major retinal abnormalities found among diabetic persons. Diabetic maculopathy is detected using image processing technique. In image processing techniques, we use image pre processing to reduce the noise and use segmentation process to extract the features of the macula. After that the features are compared using the classifier algorithm and the performances are measured using the accuracy, sensitivity and specificity.
Keywords: Classification, Retinopathy, Diabetic Maculopathy Image Processing, Segmentation.
Scope of the Article: Image Processing and Pattern Recognition