Machine Learning for Diabetic Retinopathy Detection using Image Processing
Ujwala W. Wasekar1, R. K. Bathla2
1Ujwala W. Wasekar*, Department of Computer Science, Desh Bhagat University, Mandi Gobindgarh, India.
2R. K. Bathla, Department of Computer Science, Desh Bhagat University, Mandi Gobindgarh, India.
Manuscript received on January 17, 2020. | Revised Manuscript received on January 22, 2021. | Manuscript published on January 30, 2021. | PP: 209-215 | Volume-9 Issue-5, January 2021. | Retrieval Number: 100.1/ijrte.E5267019521 | DOI: 10.35940/ijrte.E5267.019521
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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: The disorder of Diabetic Retinopathy (DR), a complication of Diabetes that may lead to blindness if not treated at an early stage, is diagnosed by evaluating the retina images of eye. However, the manual grading of images for identifying the seriousness of DR disease requires many resources and it also takes a lot of time. Automated systems give accurate results along with saving time. Ophthalmologists may find it useful in reducing their workload. Proposed work presents the method to correctly identify the lesions and classify DR images efficiently. Blood leaking out of veins form features such as exudates, microaneurysms and haemorrhages, on retina. Image processing techniques assist in DR detection. Median filtering is used on gray scale converted image to reduce noise. The features of the pre-processed images are extracted by textural feature analysis. Optic disc (OD) segmentation methodology is implemented for the removal of OD. Blood vessels are extracted using haar wavelet filters. KNN classifier is applied for classifying retinal image into diseased or healthy .The proposed algorithm is executed in MATLAB software and analyze results with regard to certain parameters such as accuracy, sensitivity, and specificity. The outcomes prove the superiority of the new method with sensitivity of 92.6%, specificity of 87.56% and accuracy of 95% on Diaretdb1 database.
Keywords: Classification, Diabetic Retinopathy, KNN, Lesions, Optic Disk segmentation.