Diabetic Retinopathy Classification Using Deep Learning Technique

EasyChair Preprint 8018

Abstract

Diabetic Retinopathy is a disease that damages the eyes and is caused by a consequence of diabetes. If blood sugar levels aren't controlled for an extended period of time, the disease can develop. It is mainly caused due to the damage of blood vessels in the retina. Retinopathy is the main  cause of blindness in the world. Doctors can diagnose blindness before it occurs using Artificial Intelligence and Deep Learning. Medical imaging plays a very crucial role in a variety of medical issues and at all major levels of health issues. Medical imaging can be used to identify a variety of common eye illnesses. However, for a variety of reasons, including uneven lighting, picture blurring, and low contrast and brightness, poor-quality retinal images are ineffective for further diagnosis, particularly in automated analysing systems. Ophthalmologists' manual Diabetic Retinopathy diagnostic procedure is time-consuming, requires more work, costly, and might result in misdiagnosis. Basing on the vision like having trouble in reading distant objects or seeing  distant objects, blindness or any other changes may happen in eye  retina that affects diabetes. Diabetic retinopathy is one of the most frequent eye illnesses, affecting mostly diabetics. This model using deep learning convolution  neural networks can assist the opthmologists by providing  clear images of the retina, and also blood vessel extracted images. There  are three phases in this diabetic retinopathy detection and  classification technique. These three phases are pre-processing,  blood vessel and exudates detection, feature extraction and  classification. In this work, From the presented retinal fundus pictures, we utilised the Res-Block model to classify and diagnose diabetic retinopathy with 92% of accuracy.

Keyphrases: Artificial Intelligence, Diabetic Retinopathy, Fundus, Medical Imaging, deep learning

@booklet{EasyChair:8018,
  author    = {Vandana Vipparthi and Sravani Mullu and Vamsipriya Patlolla and Rajeswara Rao Duvvada},
  title     = {Diabetic Retinopathy Classification Using Deep Learning Technique},
  howpublished = {EasyChair Preprint 8018},
  year      = {EasyChair, 2022}}