Classification of Non-Tumorous Facial Pigmentation Disorders using Deep Learning and SMOTE

Non-tumorous facial pigmentation, though not fatal, adversely affects one's quality of life and may indicate concurrence of systemic diseases. Automatic diagnosis method such as voting-based probabilistic discriminant analysis (V-PLDA) has been explored, but the accuracy of classification is not satisfactory due to the limited number of data for training. This paper proposes to use the pre-trained deep learning network of Inception-ResNet-v2 so that information from similar datasets can be utilized. Furthermore, data augmentation using synthetic minority over-sampling technique (SMOTE) is also applied to make full use of available training data. A clinical dataset of five most common types of non-tumorous facial pigmentation disorders in Asia, namely freckles, lentigines, melasma, Hori's nevus, and nevus of Ota, is used for training and testing. The classification accuracy has shown significant improvement (> 7%) compared to the state-of-the-art method.