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.
翻译:在皮肤疾病诊断方面,广泛探索了深相神经网络模型(DCNN)用于皮肤疾病诊断,其中一些模型取得了可比较甚至优于皮肤学家的诊断结果;然而,在皮肤疾病检测方面,对DCNN的广泛实施受到公众可获取的皮肤损伤数据集规模小和数据不平衡的阻碍;本文件提出了基于小型和不平衡数据集的皮肤损伤单一模型分类的新颖数据增强战略;首先,对不同的DCNN进行了有关该数据集的培训,以显示中度复杂度模型比较大的模型要好。第二,增加了正规化脱产模型和脱壳模型,以减少超装,并提出了修改的RandAugment增强战略,以解决小规模数据集中样本代表性不足的缺陷。最后,引入了新的多视力损失功能,以克服样本大小不均和分类难度不均的难题。通过将调整的RandAugment和多体重的焦点损失纳入一个单一的模型,我们实现了分类的准确性,与国际标准行业分类系统2018年的多个模型相仿,并提出了调整的RandAUA加强战略,以纠正小型数据集中的样本。