Artificial neural networks in radiological predictive models
Νικόλαος Δικαίος, Toru Fujiwara, David Atkinson, Shonit Punwani · UCL Discovery (University College London) · 2012
Predictive models are being increasingly employed in radiology as diagnostic aids for cancer detection. A variety of model types exist. Linear discriminant analysis (LDA) models assume linearity, normality and that the input variables are independent, assumptions which may affect classification accuracy. Neural networks (NN) whilst less intuitive, do not make these assumptions and can detect complex non-linear relationships between the input variables. Both LDA and NN are prone to over-fitting. In this work we compared the performance of multilayer perceptron (MLP) artificial NN and LDA models for prediction of transition zone (TZ) prostate cancer (based on quantitative multi-parametric MRI variables) using a leave-one-out (LOO) and a 2-fold cross validation analysis.