Machine Learning based Prediction of Retinopathy Diseases using Segmented Images

Diabetes, hypertension, obesity, glaucoma, macular degeneration, etc. are the severe and most widely spread diseases today. More ever, these diseases are the basis of several other fatal diseases. Early-stage identification and diagnosis of these diseases can prevent blindness and other life threats. Blood vessels of a retina contain information about these diseases. Therefore, features extraction from retinal vessels and classification of these diseases are essential. There are existing different approaches today to classify these diseases, but they have used RGB retinal images due to which their performances are relatively low. In this paper, we have proposed an approach based on machine learning that uses segmented retinal images generated by different efficient methods to classify diabetic retinopathy, glaucoma and multi class diseases. We have conducted exhaustive experiments on large number of images of DRIVE, STARE and HRF datasets. The accuracy of the proposed approach is 90.90%, 95.00%, and 92.90% for diabetic retinopathy, glaucoma, and multi class diseases, respectively which is found better than most of the approaches of this area.