Enhanced Skin Condition Prediction through Machine Learning Using Dynamic Training and Testing Augmentation

Abstract

In recent years, deep learning has taken the spotlight in automated medical bioimaging. However, the performance of current state-of-the-art score stems primarily from well-tuned parameters and architecture. There is still only limited research focused on dynamic data augmentation, even in the fields of machine learning and computer vision. In this study, we propose a dynamic training and testing augmentation capable of increasing performance significantly. The searching augmentation framework used in this study requires fewer GPU hours than a conventional search algorithm, which needs to train a new model every time augmentation is proposed. Speeding up of the search algorithm is achieved by using Bayesian optimization on a trained model, so we do not have to train a new model every time a new augmentation policy is proposed. The performance of our method is compared with that of a single model and the ensemble model that happens to be the winner of the ISIC 2019 challenge. Furthermore, we use the latest compact yet significantly accurate network architecture EfficientNet as the backbone system. Our method delivers a superior result, and this study also shares the searched augmentation policy utilized, which requires extraordinary resources. Thus, other researchers can use the searched augmentation policies for dermoscopic images to improve performance.