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Abstract #4029

Texture Analysis in the Characterisation of Ovarian Lesions: Use of Synthetic Minority Oversampling

Peter Gibbs 1 , Martine Dujardin 1 , and Lindsay Turnbull 1

1 Centre for MR Investigations, University of Hull, Hull, East Yorkshire, United Kingdom

T2 weighted images of ovarian lesions have been assessed using texture analysis. Uneven group sizes, which can cause bias in statistical models leading to over-fitting of the majority class, were avoided by employing synthetic minority oversampling methods. Significant differences between groups were noted for 12/16 texture parameters and a multinomial logistic regression model utilised 4 parameters (f2, f14, f15, f16) in achieving a diagnostic accuracy of 71% for a training dataset and 61% for a test dataset.

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