An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks
文献类型： 外文期刊
作者： Xu, Shipu 1 ; Liu, Chang 3 ; Zong, Yongshuo 4 ; Chen, Sirui 4 ; Lu, Yiwen 4 ; Yang, Longzhi 5 ; Ng, Eddie Y. K. 6 ; Wang, 1 ;
作者机构： 1.Tongji Univ, Dept Software Engn, Shanghai 201804, Peoples R China
2.Shanghai Acad Agr Sci, Agr Informat Inst Sci & Technol, Shanghai 201403, Peoples R China
3.Nanchang Hangkong Univ, Sch Informat Engn, Nanchang 330038, Jiangxi, Peoples R China
4.Tongji Univ, Dept Comp Sci, Shanghai 201804, Peoples R China
5.Northumbria Univ, Dept Comp & Informat Sci, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England
6.Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore
关键词： Cancer; Training; Three-dimensional displays; Convolution; Feature extraction; Computed tomography; Kernel; 2DCNNs; 3DCNNs; CT images; spatial features; spatial dynamics extracted
期刊名称：IEEE ACCESS （ 影响因子：3.367； 五年影响因子：3.671 ）
ISSN： 2169-3536
年卷期： 2019 年 7 卷
页码：
收录情况： SCI
摘要： 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.
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