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Deep learning algorithms often require thousands of training instances to generalize well. The presented research demonstrates a novel algorithm, Predict-Evaluate-Correct K-fold (PECK), that trains ensembles to learn well from limited data. The PECK algorithm is used to train a deep ensemble on 153 non-dermoscopic lesion images, significantly outperforming prior publications and state-of-the-art methods trained and evaluated on the same dataset. The PECK algorithm merges deep convolutional neural networks with support vector machine and random forest classifiers to achieve an introspective learning method. Where the ensemble is organized hierarchically, deeper layers are provided not only more training folds, but also the predictions of previous layers. Subsequent classifiers then learn and correct the previous layer errors by training on the original data with injected predictions for new data folds. In …