  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 single-model based strategy for classification of skin lesions
on small and imbalanced datasets. First, various DCNNs are trained on different
small and imbalanced datasets to verify 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 deal with the defects of sample
underrepresentation in the small dataset. Finally, a novel Multi-Weighted New
Loss (MWNL) function and an end-to-end cumulative learning strategy (CLS) are
introduced to overcome the challenge of uneven sample size and classification
difficulty and to reduce the impact of abnormal samples on training. By
combining Modified RandAugment, MWNL and CLS, our single DCNN model method
achieved the classification accuracy comparable or superior to those of
multiple ensembling models on different dermoscopic image datasets. 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.
