The other images are used for testing sets.
The obtained mean of squared error (MSE) for testing sets in present paper obtained 0.0071.
The data set is divided into training, validating and testing sets.
The database is divided into training and testing sets for experiment.
For training ANNs, the data are divided into three subsets: training, validation, and testing sets.
The performed experiments were designed into two data sets including training, and testing sets.
Also, all subsets of this database were randomly partitioned into training, validation, and testing sets.
The developed models were validated by testing sets and external independent validation sets, showing satisfactory performance.
Open image in new window Fig. 15 Accuracy of ANN predictions for training, validation, and testing sets Open image in new window Fig. 16 Histogram of calculated errors of training, validation, and testing sets.
Mixed sets have to be divided into concrete validation and testing sets before the type attribute is assigned.
We divided the patients randomly into training and testing sets with equal number in each group.
