학술논문
Classification of Non-Tumorous Facial Pigmentation Disorders using Deep Learning and SMOTE
Document Type
Conference
Source
2019 IEEE International Symposium on Circuits and Systems (ISCAS) Circuits and Systems (ISCAS), 2019 IEEE International Symposium on. :1-5 May, 2019
Subject
Language
ISSN
2158-1525
Abstract
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.