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Research Paper | Computer Science Engineering | Volume 6 Issue 7, July 2017 | Pages: 473 - 478 | India
Red Lesion Detection Using Hough Transform and KNN Classifier for Diabetic Retinopathy Screening
Abstract: Diabetic retinopathy is a major cause of blindness in the world. It will take more time to identify the clinical features such as microaneurysms, hemorrhages, exudates and cottonwool spots through manual inspection of fundus images. A computer assisted diagnosis system can help to reduce the burden on the ophthalmologist and rapidly identify the most severe cases. An efficient approach for red lesion detection in fundus image is proposed. The fundus image is preprocessed and Hough transform is used to identify the regions having red lesions. A KNN classifier is used to train the features which classifies the images according to their probability of being normal or with disease.
Keywords: Diabetic retinopathy, Microaneurysm, Hemorrhages, Thresholding, Hough Transform, KNN classifier, Automatic detection
How to Cite?: Fayiza K T, Sherin Jabbar, "Red Lesion Detection Using Hough Transform and KNN Classifier for Diabetic Retinopathy Screening", Volume 6 Issue 7, July 2017, International Journal of Science and Research (IJSR), Pages: 473-478, https://www.ijsr.net/getabstract.php?paperid=ART20174218, DOI: https://dx.doi.org/10.21275/ART20174218