Classification of Non-Tumorous Facial Pigmentation Disorders Using Improved Smote and Transfer Learning

Classification of non-tumorous facial pigmentation disorders is an important but overlooked problem. Recently, a voting-based probabilistic linear discriminant analysis (V-PLDA) method was developed to address this problem by extracting hand-craft features from a given image set of rather small size, with limited classification accuracy. In this paper, we propose an improved Synthetic Minority Over-sampling Technique (improved SMOTE) with several parameters tuned to fully utilize the available images. Moreover, transfer learning is applied to reduce the data size requirement of the deep learning model. By combining the improved SMOTE and transfer learning, a classification accuracy gain (10%) is attained compared to the state-of-the-art V-PLDA method.