Abstract
The design of neural network classifiers for the identification of diabetic retinopathy is discussed. Red-free digitised fundal images are tiled, and a neural network is trained to distinguish exudates from drusen (similar appearing lesions). By quantifying the degree of retinopathy, the approach can be used to screen diabetic patients for referral. A novel form of hierarchical feature selection using sensitivity analysis is presented. The resulting neural network is compact, and achieves 91% sensitivity and specificity on a test set.
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References
Williams, R. Diabetes mellitus. In: Stevens. A., Raftery, J. (eds). Health Care Needs Assessment. Oxford University Press, Oxford, 1994, pp. 31–57.
Singer, D.E., Nathan, D.M., Fogel, H.A. Schachat, A.P. Screening for diabetic retinopathy. Ann. Intern. Med. 1992; 116: 660–671.
R.P. Phillips, P.G. Ross, M. Tyska, P.F. Sharp and J.V. Forrester. Detection and quantification of hyperfluourescent leakage by computer analysis of fundus fluorescein angiograms. Graefe’s Arch Clin Exp Ophthalmol 1991; 229: 329–335.
T. Spencer, J.A. Olson, K.C. McHardy, P.F. Sharp and J.V. Forrester. An Image-Processing Strategy for the Segmentation and Quantification of Microaneurysms in Fluorescein Angiograms of the Ocular Fundus. Computers and Biomedical Research 1996; 29:284–302.
R.P. Phillips, J. Forester and P. Sharp. Automated detection and quantification of retinal exudates. Graefe’s Arch Ophthalmol 1993; 231: 90–94.
M.H. Goldbaum, N.P. Katz, M.R. Nelson, L.R. Haff. The discrimination of Similarly Colored Objects in Computer Images of the Ocular Fundus. Investigative Ophthalmology and Visual Science 1990; 31(4): 617–623.
Gardner, G.G., Keating, D. Williamson, T.H. and Elliot, A.T. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J. Ophthalmol. 1996; 80: 940–944.
Hunter, A. Application of Neural Networks and Sensitivity Analysis to improved prediction of Trauma Survival. Computer Methods and Algorithms in Biomedicine (in press).
T.D. Gedeon. Data Mining of Inputs: Analysing Magnitude and Functional Measures. Int. Journal of Neural Systems 1997; 8(2): 209–218.
J.M. Zurada, A. Malinowski and I. Cloete, Sensitivity Analysis for Minimization of Input Data Dimension for Feedforward Neural Network, IEEE International Symposium on Circuits and Systems, London, May 30-June 3, 1994.
A. Jain and D. Zongker Feature Selection: Evaluation, Application and Small Sample Performance. IEEE Trans. Pattern Analysis and Machine Intelligence 1997; 19(2).
Weigend, A.S., Rumelhart, D.E. and Huberman, B.A. Generalization by weight-elimination with application to forecasting. In: Lippmann, R.P., Moody, J.E. and Touretsky, D.S. (eds). Advances in Neural Information Processing Systems 1991; 3: 875–882. San Mateo, CA: Morgan Kaufmann.
M.H. Zweig and G. Campbell. Receiver-Operating Characteristic (ROC) Plots: A Fundamental Evaluation Tool in Clinical Medicine. Clin. Chem 1993; 39(4): 561–577.
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© 2000 Springer-Verlag London
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Hunter, A., Lowell, J., Owens, J., Kennedy, L., Steele, D. (2000). Quantification of Diabetic Retinopathy using Neural Networks and Sensitivity Analysis. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_10
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DOI: https://doi.org/10.1007/978-1-4471-0513-8_10
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