Open AccessDeep sparse transfer learning for remote smart tongue diagnosis
Author(s) -
Xu Zhang,
Wei Huang,
Jing Gao,
Dapeng Wang,
Chen Bai,
Zhikui Chen
Publication year - 2021
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2021063
Subject(s) - computer science , deep learning , artificial intelligence , transfer of learning , adaptation (eye) , artificial neural network , domain (mathematical analysis) , edge device , machine learning , enhanced data rates for gsm evolution , big data , deep neural networks , scheme (mathematics) , data mining , cloud computing , mathematical analysis , physics , mathematics , optics , operating system
People are exploring new ideas based on artificial intelligent infrastructures for immediate processing, in which the main obstacles of widely-deploying deep methods are the huge volume of neural network and the lack of training data. To meet the high computing and low latency requirements in modeling remote smart tongue diagnosis with edge computing, an efficient and compact deep neural network design is necessary, while overcoming the vast challenge on modeling its intrinsic diagnosis patterns with the lack of clinical data. To address this challenge, a deep transfer learning model is proposed for the effective tongue diagnosis, based on the proposed similar-sparse domain adaptation (SSDA) scheme. Concretely, a transfer strategy of similar data is introduced to efficiently transfer necessary knowledge, overcoming the insufficiency of clinical tongue images. Then, to generate simplified structure, the network is pruned with transferability remained in domain adaptation. Finally, a compact model combined with two sparse networks is designed to match limited edge device. Extensive experiments are conducted on the real clinical dataset. The proposed model can use fewer training data samples and parameters to produce competitive results with less power and memory consumptions, making it possible to widely run smart tongue diagnosis on low-performance infrastructures.