Br J Ophthalmol 96:220-223 doi:10.1136/bjophthalmol-2011-300032
  • Clinical science
  • Original article

Automated multi-level pathology identification techniques for abnormal retinal images using artificial neural networks

  1. D Jude Hemanth1
  1. 1Department of ECE, Karunya University, Coimbatore, India
  2. 2Department of ECE, Christian Engineering College, Oddanchatram, India
  3. 3Department of EEE, Karunya University, Coimbatore, India
  4. 4Department of Ophthalmology, Lotus Eye Care Hospital, Coimbatore, India
  1. Correspondence to J Anitha, Department of ECE, Karunya University, Coimbatore 641114, India; rajivee1{at}
  1. Contributors All authors included on a paper fulfil the criteria of authorship. There is no one who fulfils the criteria but has not been included as an author.

  • Accepted 18 May 2011
  • Published Online First 22 June 2011


Aim To automatically classify abnormal retinal images from four different categories using artificial neural networks with a high degree of accuracy in minimal time to assist the ophthalmologist in subsequent treatment planning.

Methods We used 420 abnormal retinal images from four different categories (non-proliferative diabetic retinopathy, central retinal vein occlusion, central serous retinopathy and central neo-vascularisation membrane). Green channel extraction, histogram equalisation and median filtering were used as image pre-processing techniques, followed by texture-based feature extraction. The application of Kohonen neural networks for pathology identification was also explored.

Results The approach described yielded an average classification accuracy of 97.7% with ±0.8% deviation for individual categories. The average sensitivity and the specificity values are 96% and 98%, respectively. The time taken by the Kohonen neural network to achieve these accurate results was 300±40 s for the 420 images.

Conclusion This study suggests that the approach described can act as a diagnostic tool for retinal disease identification. Simultaneous multi-level classification of abnormal images is possible with high accuracy using artificial neural networks. The results also suggest that the approach is time-efficient, which is essential for ophthalmologic applications.


  • Funding Foundations of Computer Science, New York, USA.

  • Competing interests None to declare.

  • Provenance and peer review Not commissioned; externally peer reviewed.

Register for free content

Free sample
This recent issue is free to all users to allow everyone the opportunity to see the full scope and typical content of BJO.
View free sample issue >>

Don't forget to sign up for content alerts so you keep up to date with all the articles as they are published.

Navigate This Article