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Automated multi-level pathology identification techniques for abnormal retinal images using artificial neural networks
  1. J Anitha1,
  2. C Kezi Selva Vijila2,
  3. A Immanuel Selvakumar3,
  4. A Indumathy4,
  5. 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}rediffmail.com

Abstract

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.

  • Pathologies
  • retinal images
  • diagnosis & ANN
  • diagnostic tests/investigation
  • imaging
  • pathology
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Footnotes

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

  • Competing interests None to declare.

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

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