  Although deep convolutional neural networks (DCNNs) have achieved significant
accuracy in skin lesion classification comparable or even superior to those of
dermatologists, practical implementation of these models for skin cancer
screening in low resource settings is hindered by their limitations in
computational cost and training dataset. To overcome these limitations, we
propose a low-cost and high-performance data augmentation strategy that
includes two consecutive stages of augmentation search and network search. At
the augmentation search stage, the augmentation strategy is optimized in the
search space of Low-Cost-Augment (LCA) under the criteria of balanced accuracy
(BACC) with 5-fold cross validation. At the network search stage, the DCNNs are
fine-tuned with the full training set in order to select the model with the
highest BACC. The efficiency of the proposed data augmentation strategy is
verified on the HAM10000 dataset using EfficientNets as a baseline. With the
proposed strategy, we are able to reduce the search space to 60 and achieve a
high BACC of 0.853 by using a single DCNN model without external database,
suitable to be implemented in mobile devices for DCNN-based skin lesion
detection in low resource settings.
