SVD and PCA features for ANN based detection of diabetes using retinopathy

  • Authors:

  • Archana Deka;Kandarpa Kumar Sarma

  • Affiliations:

  • Tezpur University, Tezpur, Assam, India;Gauhati University, Guwahati, Assam, India

  • Venue:
  • Proceedings of the CUBE International Information Technology Conference
  • Year:
  • 2012

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Abstract

Retinopathy based techniques are preferred for diabetes detection due to their advantage of bloodless diagnosis. In concert to similar works currently going on, we propose, such a diabetes detection method using Artificial Neural Network (ANN) and a feature set formed by adopting Singular Value Decomposition (SVD) and Principal Component Analysis (PCA). A robust and computationally efficient approach for the localization of the different features in a fundus retinal image is presented in this work. The work proposes certain approaches for extraction of diabetes retinopathy features using SVD and PCA and applying the composite form to ANN for training. The detection of hemorrhages and exudates are important in order to diagnose diabetes retinopathy for preventing loss of eye sight. Experimental results show that the ANN-SVD+PNN composition is a reliable means of diabetes detection with less computational complexity and high accuracy.