Automatic classification of bright retinal lesions via deep network features

Ibrahim Sadek, Mohamed Elawady, Abd El Rahman Shabayek

Research output: Working paperWorking Paper/Preprint

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The diabetic retinopathy is timely diagonalized through color eye fundus images by experienced ophthalmologists, in order to recognize potential retinal features and identify early-blindness cases. In this paper, it is proposed to extract deep features from the last fully-connected layer of, four different, pre-trained convolutional neural networks. These features are then feeded into a non-linear classifier to discriminate three-class diabetic cases, i.e., normal, exudates, and drusen. Averaged across 1113 color retinal images collected from six publicly available annotated datasets, the deep features approach perform better than the classical bag-of-words approach. The proposed approaches have an average accuracy between 91.23% and 92.00% with more than 13% improvement over the traditional state of art methods.
Original languageEnglish
Number of pages20
Publication statusPublished - 28 Jul 2017


  • diabetic retinopathy
  • exudates
  • Drusen
  • bag of words
  • convolutional neural networks
  • support vector machine
  • bright retinal lesions
  • deep network features


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