Detection and Classification of Hard Exudates with Fundus Images Complements and Neural Networks

Muhammad Altaf Hussain, Syed Osama Bin Islam, M. I. Tiwana, Ubaid-Ur-Rehman, W. S. Qureshi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

Diabetic Retinopathy (DR) is an eye disorder that progressively leads to vision loss due to high glucose causing impairment of retinal blood vessels (BVs). 'Retinal Bright Lesions' such as 'Hard Exudates' (HEs) are plasma leakages from rapture retinal capillaries. HEs appear as hard, waxy, yellowish deposits from tiny spots to fat patches and signify moderate-severe Non-Proliferative Diabetic Retinopathy (NPDR). This paper proposes a simple, compact and computationally inexpensive technique for detection and classification of HEs using Digital Image Processing Techniques on digital fundus images complements and Artificial Neural Networks (ANN). The proposed technique unfolds through five stages i.e. Pre-processing, coarse detection, optimization, features detection extraction followed by classification. 'Speed Up Robust Features' (SURF) algorithm has been used for features detection extraction while 'Feed-Forward Back-propagation' (FFBP) ANN has been used for classification. The proposed technique has yielded 98.7% 'Sensitivity' (SE), 97.5% 'Specificity' (SP) and 97.7% 'Accuracy' (AC) on 'DIARETDB1' fundus images.

Original languageEnglish
Title of host publication2019 5th International Conference on Control, Automation and Robotics, ICCAR 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages206-211
Number of pages6
ISBN (Electronic)9781728133263
DOIs
Publication statusPublished - Apr 2019
Externally publishedYes
Event5th International Conference on Control, Automation and Robotics, ICCAR 2019 - Beijing, China
Duration: 19 Apr 201922 Apr 2019

Publication series

Name2019 5th International Conference on Control, Automation and Robotics, ICCAR 2019

Conference

Conference5th International Conference on Control, Automation and Robotics, ICCAR 2019
Country/TerritoryChina
CityBeijing
Period19/04/1922/04/19

Keywords

  • artificial neural networks
  • back-propagation
  • confusion matrix
  • diabetic retinopathy
  • green channel
  • hard exudates
  • image complements
  • optimization
  • thresholding

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