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

Xu, Shipu, Liu, Chang, Zong, Yongshuo, Chen, Sirui, Lu, Yiwen, Yang, Longzhi, Ng, Eddie Y. K., Wang, Yongtong, Wang, Yunsheng, Liu, Yong, Hu, Wenwen and Zhang, Chenxi (2019) An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks. IEEE Access, 7. pp. 158603-158611. ISSN 2169-3536

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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.

Item Type: Article
Subjects: B800 Medical Technology
G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 14 Nov 2019 15:24
Last Modified: 31 Jul 2021 22:15

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