The diabetic retinopathy (DR) is a clinical disorder of retina caused due to diabetes mellitus. This work presents an automated detection of DR images using curvelet and retina analyser. Like Fourier transform, curvelet is a mathematical transform. It is deployed here to trace the directional field of the curve singularities of the retina images. This helps to segment the retinal vasculature of the fundus images. The change in retinal morphology like length, diameter, tortuosity due to the ophthalmoscopic changes are computed by retina analyser. Feedforward neural network (FNN) is implemented to detect DR images with sensitivity: 79%, specificity: 94% and accuracy: 88% which is better than the contemporary works. The proposed system is a smart integration of three modules – curvelet, retina analyser, and FNN. It is simple, less time consuming and easily implementable. In future the same system can be extended to detect exact stage of DR. Copyright © 2024 Inderscience Enterprises Ltd.
