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There are a lot of hidden dangers in the change of human skin conditions,such as the sunburn caused by long-time exposure to ultraviolet radiation,which not only has aesthetic impact causing psychological depression and lackof self-confidence, but also may even be life-threatening due to skincanceration. Current skin disease researches adopt the auto-classificationsystem for improving the accuracy rate of skin disease classification. However,the excessive dependence on the image sample database is unable to provideindividualized diagnosis service for different population groups. To overcomethis problem, a medical AI framework based on data width evolution andself-learning is put forward in this paper to provide skin disease medicalservice meeting the requirement of real time, extendibility andindividualization. First, the wide collection of data in the close-loopinformation flow of user and remote medical data center is discussed. Next, adata set filter algorithm based on information entropy is given, to lighten theload of edge node and meanwhile improve the learning ability of remote cloudanalysis model. In addition, the framework provides an external algorithm loadmodule, which can be compatible with the application requirements according tothe model selected. Three kinds of deep learning model, i.e. LeNet-5, AlexNetand VGG16, are loaded and compared, which have verified the universality of thealgorithm load module. The experiment platform for the proposed real-time,individualized and extensible skin disease recognition system is built. And thesystem's computation and communication delay under the interaction scenariobetween tester and remote data center are analyzed. It is demonstrated that thesystem we put forward is reliable and effective.

AI-Skin : Skin Disease Recognition based on Self-learning and Wide Data Collection through a Closed Loop Framework