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

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
33 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)158603-158611
JournalIEEE Access
Volume7
Early online date30 Oct 2019
DOIs
Publication statusPublished - 12 Nov 2019

Fingerprint

Dive into the research topics of 'An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks'. Together they form a unique fingerprint.

Cite this