Diabetic Retinopathy using morphological operations and machine learning

Diabetic Retinopathy that is DR which is a eye disease that affect retina and further later at severe stage it lead to vision loss. Early detection of DR is helpful to improve the screening of patient to prevent further damage. Retinal micro-aneurysms, haemorrhages, exudates and cotton wool spots are kind of major abnormality to find the Non- Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). The main objective of our proposed work is to detect retinal micro-aneurysms and exudates for automatic screening of DR using Support Vector Machine (SVM) and KNN classifier. To develop this proposed system, a detection of red and bright lesions in digital fundus photographs is needed. Micro-aneurysms are the first clinical sign of DR and it appear small red dots on retinal fundus images. To detect retinal micro-aneurysms, retinal fundus images are taken from Messidor, DB-rect dataset. After pre-processing, morphological operations are performed to find micro-aneurysms and then features are get extracted such as GLCM and Structural features for classification. In order to classify the normal and DR images, different classes must be represented using relevant and significant features. SVM gives better performance over KNN classifier.