Neural Computing Based Abnormality Detection in Retinal Optical Images

Automated eye disease identification systems facilitate the ophthalmologists in accurate diagnosis and treatment planning. In this paper, an automated system based on artificial neural network is proposed for eye disease classification. Abnormal retinal images from four different classes namely non-proliferative diabetic retinopathy (NPDR), Central retinal vein occlusion (CRVO), Choroidal neovascularisation membrane (CNVM) and central serous retinopathy (CSR) are used in this work. A suitable feature set is extracted from the pre-processed images and fed to the classifier, Classification of the four eye diseases is performed using the supervised neural network namely back propagation neural network (BPN). Experimental results show promising results for the back propagation neural network as a disease classifier. The results are compared with the statistical classifier namely minimum distance classifier to justify the superior nature of neural network based classification.