Small sample-based disease diagnosis model acquisition in medical human-centered computing

Abstract

With the development of wireless communications and networks, HCC (human-centred computing) has attracted considerable attention in recent years throughout the medical field. HCC can provide an effective integration of various medical auxiliary diagnosis models using machine learning algorithms. In medical HCC, deep learning has demonstrated its powerful ability in the field of computer vision. However, image processing based on deep learning usually requires a large amount of labeled data, which requires significant resources since it needs to be completed by doctors, and it is difficult to collect a large amount of data for some rare diseases. Therefore, how to use the deep learning method to obtain an effective auxiliary diagnosis model based on a small sample or zero sample data set has become an important issue in the study of medical auxiliary diagnosis. We proposes an auxiliary diagnosis model acquisition method based on a variational auto-encoder and zero sample augmentation technology, and the incremental update training program based on wireless communications and networks is designed to obtain the auxiliary diagnosis model to solve the difficulty of collecting a large amount of valid data. The experimental results show that the model obtained by the above method based on a small sample or zero sample data set can effectively diagnose the types of skin diseases, which helps doctors make better judgments.