Discriminative convolution neural network architecture for diagnosis of diabetic retinopathy through classification and progression prediction of lesions grading in color fundus images of retina
International Journal of Health Sciences
Diabetic Retinopathy (DR) is a common complication of diabetes mellitus, which causes lesions on the retina that effect vision. Many researchers have supported the ophthalmologists to diagnosis and classify the stage of the disease in the retinal fundus images using machine learning model. Machine learning models are less impressive for several staging diseases due to clinical grading. In order to develop a system capable of classifying the lesion grading images on disease pathology, a unique
... ep learning architecture named as discriminative Convolution Neural Network has been employed towards diagnosis of diabetic retinopathy through classification and progression prediction of lesion grading in fundus image of Retina. Initially Image Pre-processing has been carried out using wiener filter to remove the noise and Contrast limited adaptive histogram equalization for image enhancement. Pre-processed image has been processed using Oriented Fast and Rotated Brief for feature descriptors. It contains like Optic distance, Fovea, blood Vessel, Blot haemorrhages, Exudate number, exudates area, Macular Edema, Bifurcation, Shannon entrophy, Kapur Entropy and Renyis Entropy, LBP entropy, LBP energy and Microaneurysm. On these obtained features, feature reduction has to be carried out using principle component analysis to eliminate the irrelevant features before the onset of the process.