Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145837
Title: An early diagnosis of oral cancer based on three-dimensional convolutional neural networks
Authors: Xu, Shipu
Liu, Chang
Zong, Yongshuo
Chen, Sirui
Lu, Yiwen
Yang, Longzhi
Ng, Eddie Yin Kwee
Wang, Yongtong
Wang, Yunsheng
Liu, Yong
Hu, Wenwen
Zhang, Chenxi
Keywords: Engineering::Mechanical engineering
Issue Date: 2019
Source: Xu, S., Liu, C., Zong, Y., Chen, S., Lu, Y., Yang, L., . . . Zhang, C. (2019). An early diagnosis of oral cancer based on three-dimensional convolutional neural networks. IEEE Access, 7, 158603-158611. doi:10.1109/ACCESS.2019.2950286
Journal: IEEE Access
Abstract: 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.
URI: https://hdl.handle.net/10356/145837
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2950286
Rights: © 2020 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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