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Automatic Identification and Classification of Microaneurysms, Exudates and Blood Vessel for Early Diabetic Retinopathy Recognition

  • Vaibhav V. KambleEmail author
  • Rajendra D. Kokate
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 711)

Abstract

Diabetic retinopathy (DR) is vital concern that leads to blindness in adults around the world. In this paper, we proposed a system for early identification and classification of retinal fundus images as DR or non-DR. The ophthalmic features like blood vessels, microaneurysms and exudates are extracted and calculated by applying morphological of 2D median filter, multilevel histogram analysis and intensity transformation, respectively. The proposed system is executed on DIARETDB0 130 and DIARETDB1 89 fundus images dataset using artificial neural networks (ANNs). Result analysis is completed by calculating mean, variance, standard deviation, and correlation. We trained the proposed system model by multilayer perceptron with back-propagation, and system achieved sensitivity 0.83 and specificity 0.045 for DIARETDB0 and sensitivity 0.95 and specificity 0.2 for DIARETDB1.

Keywords

Diabetic retinopathy Blood vessels Microaneurysms Exudates Tortuosity ANN Fundus images 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electronics and Tele-communicationDr. Babasaheb Ambedkar Marathwada UniversityAurangabadIndia
  2. 2.Department of Instrumentation EngineeringGovernment College of EngineeringJalgaonIndia

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