he 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.
Machine Learning for Diabetic Retinopathy Detection using Image Processing