Open AccessReal‐time classification on oral ulcer images with residual network and image enhancement
Author(s) -
Guo Jianbin,
Wang Haolin,
Xue Xingsi,
Li Mengting,
Ma Zhongxiong
Publication year - 2022
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12144
Subject(s) - overfitting , artificial intelligence , residual , computer science , deep learning , transfer of learning , contextual image classification , machine learning , pattern recognition (psychology) , image (mathematics) , sensitivity (control systems) , artificial neural network , algorithm , electronic engineering , engineering
With the advances of deep learning research in the past few years, healthcare and smart medicines have been significantly developed. Inspired by the wide application of deep learning in medical image classification and disease diagnosis, this paper further proposes a variant of the Residual Network framework to classify the oral ulcer images in real‐time. In particular, image pre‐processing and enhancement techniques are used to enrich the datasets and reduce model overfitting. Besides, the transfer learning is further introduced into the residual blocks to improve the classification accuracy, with the later layers trained from the labeled datasets. To validate the performance of authors' proposal, it is compared with other classic deep learning models with respect to the classification sensitivity, specificity, and accuracy. The experimental results show that authors' approach outperforms those classic classification networks when the oral ulcers are classified and diagnosed in real‐time.