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

TL;DR

This paper introduces a self-learning, wide-data collection framework for skin disease recognition that enhances accuracy, individualization, and real-time performance by integrating data collection, filtering, and multiple deep learning models.

Contribution

It proposes a novel closed-loop AI framework with data width evolution and self-learning, enabling personalized skin disease diagnosis and efficient model deployment.

Findings

The system achieves reliable real-time skin disease recognition.

Data filtering improves model learning efficiency.

Multiple deep learning models demonstrate versatility.

Abstract

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.