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Scheme of the nested cross validation method used to evaluate the different datasets of features. Texture data extracted from the PTC images were randomly divided into training and test sets N = 100 times to evaluate the 3 model using different of samples to obtain averaged results (a) T2WI-FS images with the average accuracy, (b) T2WI images with the average accuracy. Changes of average accuracy with the increase in feature number during feature selection.
