Automated multi-level pathology identification techniques for abnormal retinal images using artificial neural networks
- 1Department of ECE, Karunya University, Coimbatore, India
- 2Department of ECE, Christian Engineering College, Oddanchatram, India
- 3Department of EEE, Karunya University, Coimbatore, India
- 4Department of Ophthalmology, Lotus Eye Care Hospital, Coimbatore, India
- Correspondence to J Anitha, Department of ECE, Karunya University, Coimbatore 641114, India;
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.