Machine Learning Techniques used for Recognizing the Diabetic Retinopathy: A Survey

Abstract

Diabetic Retinopathy (DR) is one of the major causes of blindness. DR mutilates the retinal blood vessels of a patient having diabetes. The DR has two major types: First one is Non- Proliferative Diabetic Retinopathy (NPDR) and second is Proliferative Diabetic Retinopathy (PDR). PDR is the advanced stage of DR which leads to neo vascularization, it is expected that the number of DR patients is to increase from 382 million to 592 million by 2028. In the early stages of the DR the patients were asymptomatic but in advanced stages, it leads to floaters, blurred vision, distortions, and progressive visual acuity loss. It is difficult but utmost important to detect the DR in early stages to avoid the worse effect of latter stages. The colour fundus images were used for the diagnosis of DR, the manual analysis could only be done by highly trained domain experts but it is bit expensive in terms of time and cost. Hence, it is important to use computer vision methods to automatically analyse the fundus images of Retina and assist the physicians/radiologists. The computer vision-based methods are divided into hand-on engineering and end-to-end learning. The hand-on engineering methods extract features using traditional approaches such as HoG, SIFT, LBP, Gabor filters, which failed to encode the variations in scale, rotation, and illumination. The end-to-end leaning automatically learns the hidden rich features and thus performs better classification.