Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model

K. Shankar*, Abdul Rahaman Wahab Sait, Deepak Gupta, S. K. Lakshmanaprabu, Ashish Khanna, Hari Mohan Pandey

*Corresponding author for this work

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

In recent days, the incidence of Diabetic Retinopathy (DR)has become high, affecting the eyes because of drastic increase in the glucose level in blood. Globally, almost half of the people under the age of 70 gets severely affected by diabetes. In the absence of earlier recognition and proper medication, the DR patients tend to lose their vision. When the warning signs are tracked down, the severity level of the disease has to be validated so to take decisions regarding appropriate treatment further. The current research paper focuses on the concept of classification of DR fundus images on the basis of severity level using a deep learning model. This paper proposes a deep learning-based automated detection and classification model for fundus DR images. The proposed method involves various processes namely preprocessing, segmentation and classification. The methods begins with preprocessing stage in which unnecessary noise that exists in the edges is removed. Next, histogram-based segmentation takes place to extract the useful regions from the image. Then, Synergic Deep Learning (SDL) model was applied to classify the DR fundus images to various severity levels. The justification for the presented SDL model was carried out on Messidor DR dataset. The experimentation results indicated that the presented SDL model offers better classification over the existing models.

Original languageEnglish
Pages (from-to)210-216
Number of pages7
JournalPattern Recognition Letters
Volume133
Early online date3 Mar 2020
DOIs
Publication statusE-pub ahead of print - 3 Mar 2020

Keywords

  • Classification
  • Deep learning
  • Diabetic retinopathy
  • Messidor dataset
  • Synergic deep learning

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