Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance Images

Figure 5

Nested cross validation: in the inner loop, the performance of different sets of classifier parameters and features is estimated based on a bootstrap cross validation. The optimal classifier parameters and features are selected based on the performance evaluation over several bootstrap rounds. In the outer loop, model performance of the optimized classifier parameters and features is evaluated on the hold-out test set in the outer loop. The outer loop is repeated several times, every time with potentially different classifier parameters and features.