  Deep convolutional neural network (DCNN) models have been widely explored for
skin disease diagnosis and some of them have achieved the diagnostic outcomes
comparable or even superior to those of dermatologists. However, broad
implementation of DCNN in skin disease detection is hindered by small size and
data imbalance of the publically accessible skin lesion datasets. This paper
proposes a novel data augmentation strategy for single model classification of
skin lesions based on a small and imbalanced dataset. First, various DCNNs are
trained on this dataset to show that the models with moderate complexity
outperform the larger models. Second, regularization DropOut and DropBlock are
added to reduce overfitting and a Modified RandAugment augmentation strategy is
proposed to address the defects of sample underrepresentation in the small
dataset. Finally, a novel Multi-Weighted Focal Loss function is introduced to
overcome the challenge of uneven sample size and classification difficulty. By
combining Modified RandAugment and Multi-weighted Focal Loss in a single DCNN
model, we have achieved the classification accuracy comparable to those of
multiple ensembling models on the ISIC 2018 challenge test dataset. Our study
shows that this method is able to achieve a high classification performance at
a low cost of computational resources and inference time, potentially suitable
to implement in mobile devices for automated screening of skin lesions and many
other malignancies in low resource settings.
