An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks

Shipu Xu; Chang Liu; Yongshuo Zong; Sirui Chen; Yiwen Lu; Longzhi Yang; Eddie Y. K. Ng; Yongtong Wang; Yunsheng Wang; Yong Liu; Wenwen Hu; Chenxi Zhang

doi:10.1109/ACCESS.2019.2950286

IEEE Access (Jan 2019)

An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks

Shipu Xu,
Chang Liu,
Yongshuo Zong,
Sirui Chen,
Yiwen Lu,
Longzhi Yang,
Eddie Y. K. Ng,
Yongtong Wang,
Yunsheng Wang,
Yong Liu,
Wenwen Hu,
Chenxi Zhang

Affiliations

Shipu Xu: ORCiD; Department of Software Engineering, Tongji University, Shanghai, China
Chang Liu: ORCiD; School of Information Engineering, Nanchang Hangkong University, Nanchang, China
Yongshuo Zong: Department of Computer Science, Tongji University, Shanghai, China
Sirui Chen: Department of Computer Science, Tongji University, Shanghai, China
Yiwen Lu: Department of Computer Science, Tongji University, Shanghai, China
Longzhi Yang: ORCiD; Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, U.K.
Eddie Y. K. Ng: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
Yongtong Wang: Department of Computer Science, Tongji University, Shanghai, China
Yunsheng Wang: Agricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai, China
Yong Liu: Agricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai, China
Wenwen Hu: Agricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai, China
Chenxi Zhang: ORCiD; Department of Software Engineering, Tongji University, Shanghai, China

DOI: https://doi.org/10.1109/ACCESS.2019.2950286
Journal volume & issue: Vol. 7
pp. 158603 – 158611

Abstract

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Three-dimensional convolutional neural networks (3DCNNs), a rapidly evolving modality of deep learning, has gained popularity in many fields. For oral cancers, CT images are traditionally processed using two-dimensional input, without considering information between lesion slices. In this paper, we established a 3DCNNs-based image processing algorithm for the early diagnosis of oral cancers, which was compared with a 2DCNNs-based algorithm. The 3D and 2D CNNs were constructed using the same hierarchical structure to profile oral tumors as benign or malignant. Our results showed that 3DCNNs with dynamic characteristics of the enhancement rate image performed better than 2DCNNS with single enhancement sequence for the discrimination of oral cancer lesions. Our data indicate that spatial features and spatial dynamics extracted from 3DCNNs may inform future design of CT-assisted diagnosis system.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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