title: Automated multi-level pathology identification techniques for abnormal retinal images using artificial neural networks.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of ECE, Karunya University, Coimbatore 641114, India. rajivee1@rediffmail.com
authors.forename: J
authors.initials: J
authors.lastname: Anitha
authors.forename: C Kezi Selva
authors.initials: CK
authors.lastname: Vijila
authors.forename: A Immanuel
authors.initials: AI
authors.lastname: Selvakumar
authors.forename: A
authors.initials: A
authors.lastname: Indumathy
authors.forename: D
authors.initials: D
authors.lastname: Jude Hemanth
mesh_terms: D056833:Central Serous Chorioretinopathy / Q000175:diagnosis*; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D005189:False Positive Reactions; D006801:Humans; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012160:Retina / Q000473:pathology; D015861:Retinal Neovascularization / Q000175:diagnosis*; D012170:Retinal Vein Occlusion / Q000175:diagnosis*; D012680:Sensitivity and Specificity