AI-Skin: Skin disease recognition based on self-learning and wide data collection through a closed-loop framework

• Published in: Information fusion Vol. 54; pp. 1 - 9
• Main Authors: Chen, Min, Zhou, Ping, Wu, Di, Hu, Long, Hassan, Mohammad Mehedi, Alamri, Atif
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2020
• Subjects: Data width evolution; Deep learning model; Self-learning process; Skin disease recognition; Data width evolution; Self-learning process; Skin disease recognition; Deep learning model
• ISSN: 1566-2535; 1872-6305
• DOI: 10.1016/j.inffus.2019.06.005

•Early detection of skin disease is critical.•It’s important to provide individualized diagnosis service for different groups.•A medical AI framework based on data width evolution and self-learning is proposed.•AI Skin prototype system is built and demonstrated its effectiveness. 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 lack of self-confidence, but also may even be life-threatening due to skin canceration. Current skin disease researches adopt the auto-classification system for improving the accuracy rate of skin disease classification. However, the excessive dependence on the image sample database is unable to provide individualized diagnosis service for different population groups. To overcome this problem, a medical AI framework based on data width evolution and self-learning is put forward in this paper to provide skin disease medical service meeting the requirement of real time, extendibility and individualization. First, the wide collection of data in the close-loop information flow of user and remote medical data center is discussed. Next, a data set filter algorithm based on information entropy is given, to lighten the load of edge node and meanwhile improve the learning ability of remote cloud analysis model. In addition, the framework provides an external algorithm load module, which can be compatible with the application requirements according to the model selected. Three kinds of deep learning model, i.e., LeNet-5, AlexNet and VGG16, are loaded and compared, which have verified the universality of the algorithm load module. The experiment platform for the proposed real-time, individualized and extensible skin disease recognition system is built. And the system’s computation and communication delay under the interaction scenario between tester and remote data center are analyzed. It is demonstrated that the system we put forward is reliable and effective.