人工智能
卷积神经网络
计算机科学
模式识别(心理学)
分割
Gabor滤波器
图像纹理
纹理(宇宙学)
灵敏度(控制系统)
计算机视觉
图像分割
特征提取
图像(数学)
工程类
电子工程
作者
Chih-Hung Chan,Tze Ta Huang,Chih-Yang Chen,Chien-Cheng Lee,Man-Yee Chan,Pau-Choo Chung
出处
期刊:IEEE Transactions on Biomedical Circuits and Systems
[Institute of Electrical and Electronics Engineers]
日期:2019-05-22
卷期号:13 (4): 766-780
被引量:24
标识
DOI:10.1109/tbcas.2019.2918244
摘要
The paper proposes an innovative deep convolutional neural network (DCNN) combined with texture map for detecting cancerous regions and marking the ROI in a single model automatically. The proposed DCNN model contains two collaborative branches, namely an upper branch to perform oral cancer detection, and a lower branch to perform semantic segmentation and ROI marking. With the upper branch the network model extracts the cancerous regions, and the lower branch makes the cancerous regions more precision. To make the features in the cancerous more regular, the network model extracts the texture images from the input image. A sliding window is then applied to compute the standard deviation values of the texture image. Finally, the standard deviation values are used to construct a texture map, which is partitioned into multiple patches and used as the input data to the deep convolutional network model. The method proposed by this paper is called texture-map-based branch-collaborative network. In the experimental result, the average sensitivity and specificity of detection are up to 0.9687 and 0.7129, respectively based on wavelet transform. And the average sensitivity and specificity of detection are up to 0.9314 and 0.9475, respectively based on Gabor filter.
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