Comparative Study and Detection of Diabetic Retinopathy in Retinal Images Using Computational Approach

EasyChair Preprint 5725

Abstract

Retinal image segmentation and classification is a challenging task in diagnosing and treating Diabetic Retinopathy (DR) over the past decade. Usually, a retinal image is used to assess diabetic diseases, as it offers complementary information for acquiring the retinal image sequences. This long outstanding problem to classify the DR significantly requires more time for a physician. Therefore, developed an automated computational approach for physicians with less time and speed up the diagnosing procedure. The proposed work based on machine learning techniques for achieving blood vessel classification using the optic disc segmented features of the retinal image. The segments are generated through the image processing mechanism, which ensures the effectiveness of optimal segment selection that yields to detect the optic disc and blood vessel more accurately. In, this proposed work detailed comparative study for image processing and machine learning techniques in DR are analyzed.  Finally, the effectiveness of the proposed work is carried out by using these various machine learning algorithms and attained a better performance value. The proposed work achieves the best results values for blood vessel classification in DR and computed the performance metrics in terms of accuracy, sensitivity, and specificity respectively.

Keyphrases: Classification, Diabetic Diseases, Optic Disc, Severity Level., blood vessel

@booklet{EasyChair:5725,
  author    = {Godfrin Reethas and S Suganthi Devi},
  title     = {Comparative Study and Detection of Diabetic Retinopathy in Retinal Images Using Computational Approach},
  howpublished = {EasyChair Preprint 5725},
  year      = {EasyChair, 2021}}