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DReAM: Diabetic Retinopathy Analysis using Machine Learning

EasyChair Preprint no. 3817

6 pagesDate: July 12, 2020


The Diabetic Retinopathy(D.R) is an eye disease which occurs due to changes in blood vessels of retina because the patient is suffering from diabetes. It is the most common cause of blindness in the world. To avoid blindness caused by diabetes, the detection of diabetic retinopathy as quickly and early as possible is the only option as number of persons becoming blind because of this disease are huge and can’t be done much if it is detected at a later stage. In this review paper we have focused on automatic detection of diabetic retinopathy through detecting retinal condition and also classify the stage of the (D.R). We studied various methods available to detect the (D.R). Detecting D.R early so that treatment on diabetic retinopathy can be done and blindness caused by it can be avoided. Decision making for the severity level of disease was performed by collaborating KNN and LBP algorithm which gives accuracy and reduces time of diagnosis of diabetic retinopathy(D.R).This paper presents review on various technique used for detection and diagnosis of diabetic retinopathy.

Keyphrases: Diabetic Retinopathy, feature extraction, KNN, LBP, Local Binary Pattern, Retinopathy

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Devendra Kumar Misra and Shubham Kumar Singh and Mohit Bhambri and Krishna Pandey},
  title = {DReAM: Diabetic Retinopathy Analysis using Machine Learning},
  howpublished = {EasyChair Preprint no. 3817},

  year = {EasyChair, 2020}}
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