  Skin disease is one of the most common types of human diseases, which may
happen to everyone regardless of age, gender or race. Due to the high visual
diversity, human diagnosis highly relies on personal experience; and there is a
serious shortage of experienced dermatologists in many countries. To alleviate
this problem, computer-aided diagnosis with state-of-the-art (SOTA) machine
learning techniques would be a promising solution. In this paper, we aim at
understanding the performance of convolutional neural network (CNN) based
approaches. We first build two versions of skin disease datasets from Internet
images: (a) Skin-10, which contains 10 common classes of skin disease with a
total of 10,218 images; (b) Skin-100, which is a larger dataset that consists
of 19,807 images of 100 skin disease classes. Based on these datasets, we
benchmark several SOTA CNN models and show that the accuracy of skin-100 is
much lower than the accuracy of skin-10. We then implement an ensemble method
based on several CNN models and achieve the best accuracy of 79.01\% for
Skin-10 and 53.54\% for Skin-100. We also present an object detection based
approach by introducing bounding boxes into the Skin-10 dataset. Our results
show that object detection can help improve the accuracy of some skin disease
classes.
