Classification of Diabetic Maculopathy from Retinal Images

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Diabetes mellitus is a significant cause for   visible loss and vision deficit. All patients with type 1  diabetes and greater than 60% of type 2 diabetes suffer a few degrees of retinopathy, due to diabetes for a long time. The damage of the normal vision, contingent upon the significant of damage of the macula, is due to diabetes retinopathy, which extends to Maculopathy. The main objective of  this work is to design  a  method and develop software to identify the seriousness of  diabetic maculopathy, using image processing techniques on retinal images. The proposed framework classifies different types of maculopathy as Normal or  clinically important and  non-clinically significant maculopathy from  fundus images. The features had been separated from the original fundus image with morphological operations and strengthened with two classifiers, the Artificial Neural method (ANN) and probabilistic neural methods (PNN). The proposed method  established that ANN has the best characterization performance efficiency of 96.67%  compared  to PNN.

     

     

     

  • Keywords


    Image processing, classification, Diabetic maculopathy, Feature extraction, macular Segmentation.

  • References


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Article ID: 28708
 
DOI: 10.14419/ijet.v7i4.22.28708




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