Fig. 18.

Leave-one-out cross-validation using pre-trained networks as feature extractor and majority voting for final classification results. The experiments were performed 33 times and each time, one patient was left out for test set and the classifier was trained on the OCT images of the remaining patients. Measured accuracies obtained from all the patients are sorted from lower to higher accuracy.