Self Organizing neural network based pathology classification in retinal images

Abstract

Artificial neural networks are significantly used in the field of ophthalmology for accurate disease identification which further aids in treatment planning. In this paper, an automated system based on Self-Organizing neural network (Kohonen network) is proposed for eye disease classification. Abnormal retinal images from four different classes namely nonproliferative 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 unsupervised neural network. Experimental results show promising results for the Kohonen 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.