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A multi-level deep ensemble (MLDE) model that can be trained in an 'end toend' manner is proposed for skin lesion classification in dermoscopy images. Inthis model, four pre-trained ResNet-50 networks are used to characterize themultiscale information of skin lesions and are combined by using an adaptiveweighting scheme that can be learned during the error back propagation. Theproposed MLDE model achieved an average AUC value of 86.5% on the ISIC-skin2018 official validation dataset, which is substantially higher than theaverage AUC values achieved by each of four ResNet-50 networks.

A Multi-Level Deep Ensemble Model for Skin Lesion Classification in Dermoscopy Images