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K.A. Vermeer, N.J. Reus, F.M. Vos, A.M. Vossepoel, H.G. Lemij; Classifying Scanning Laser Polarimetry Images by a Limited Number of Features . Invest. Ophthalmol. Vis. Sci. 2006;47(13):3340.
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© ARVO (1962-2015); The Authors (2016-present)
The built–in classifier (NFI) of the GDx VCC (Carl Zeiss Meditec, Inc., Dublin, CA) that classifies scans as healthy or glaucomatous on a scale of 1–100 is a linear support vector (SV) machine that depends on a large number (87) of features derived from the scans. A classifier based on only a few features may be more intuitive to the clinician, without adversely affecting the classification results. This study compares the cross–validated accuracy of a linear SV classifier based on many features with one based on only a few features for the classification of SLP images.
71 healthy and 71 glaucomatous eyes were included. The optimal cut–off value and the corresponding accuracy of the built–in classifier were determined. A linear SV classifier based on all 87 features, thereby mimicking the built–in classifier, was trained and tested. A linear SV classifier based on a limited number of features (1–6) was trained and tested. All errors were estimated by cross–validation to avoid biased results.
The optimal cut–off value for the NFI was 42, resulting in an error of 9.2%. A linear SV classifier, trained on all 87 features of the available data, gave an error of 5.0% (SD 0.5%). Reducing the number of features to four hardly increased the error; the estimation of the error of this classifier was 5.9% (SD 2.5%). All results of the limited feature sets are tabulated below.
Comparing the errors of the retrained SV classifier and the four–feature SV classifier shows no significant difference, although the error of the 87–feature classifier has a somewhat smaller variance. Adding more features to the four–feature classifier did not significantly reduce the estimated error of 5.9%.
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