Deep Learning Classification on Optical Coherence Tomography Retina Images

Frank Y. Shih, Himanshu Patel

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


This paper presents a novel deep learning classification technique applied on optical coherence tomography (OCT) retinal images. We propose the deep neural networks based on Vgg16 pre-trained network model. The OCT retinal image dataset consists of four classes, including three most common retina diseases and one normal retina scan. Because the scale of training data is not sufficiently large, we use the transfer learning technique. Since the convolutional neural networks are sensitive to a little data change, we use data augmentation to analyze the classified results on retinal images. The input grayscale OCT scan images are converted to RGB images using colormaps. We have evaluated different types of classifiers with variant parameters in training the network architecture. Experimental results show that testing accuracy of 99.48% can be obtained as combined on all the classes.

Original languageEnglish (US)
Article number2052002
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number8
StatePublished - Jul 1 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


  • Deep learning
  • OCT retinal images
  • convolutional neural network
  • image classification
  • transfer learning


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