Deformation and Refined Features Based Lesion Detection on Chest X-Ray

doi:10.1109/ACCESS.2020.2963926

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	Deformation and Refined Features Based Lesion Detection on Chest X-Ray
	Li, Ce1 ; Zhang, Dong1 ; Du, Shaoyi 2; Tian, Zhiqiang 3
	2020
发表期刊	IEEE Access
ISSN	2169-3536
卷号	8 期号:8 页码:14675-14689
摘要	Automatic and accurate detection of chest X-ray lesion is a challenging task. In the chest X-ray image, the lesions are shown with blurred boundary contours, different sizes, variable shapes, uneven density, etc. Besides, the deep convolutional neural network (CNN) consists of traditional convolution units, which has the limitations of rectangular sampling. The CNN extracts difficultly the deformation and refined features of chest X-ray lesions. Because of these factors, the accuracy of the lesion detection algorithm is not high. To deal with problems, we propose the deformation and refined features based lesion detection on the chest X-ray algorithm called DRCXNet. Firstly, the deformable convolution with amplitude modulation (AMDCN) is built to extract the deformation features of the lesions on the chest X-ray. Secondly, to obtain the refined feature, the global features and local features are fused, which can enrich the feature space of the lesion. Thirdly, the pooling layer combines with the AMDCN and region proposal network to establish the deformable pooling layer, which enhances the model's sensitivity to the lesion location. During the training, the model is optimized by the improved regression loss function with a gradient control factor. On the public datasets RSNA and ChestX-ray8, the proposed method outperforms seven popular detection algorithms. The proposed method is a significant performance in both qualitative and quantitative experiments. Its comprehensive evaluation scores, sensitivity, precision, and the mean dice similarity coefficient are 0.866, 0.914, 0.836 and 0.859 respectively. The proposed algorithm achieves a very satisfactory result. © 2013 IEEE.
关键词	Convolution Convolutional neural networks Deep neural networks Deformation Signal detection
DOI	10.1109/ACCESS.2020.2963926
URL	查看原文
收录类别	SCI ; SCIE ; EI
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000524736700027
出版者	Institute of Electrical and Electronics Engineers Inc., United States
EI入藏号	20200908221310
EI主题词	Feature extraction
EI分类号	716.1 Information Theory and Signal Processing
来源库	WOS
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/109744
专题	新能源学院能源与动力工程学院电气工程与信息工程学院
通讯作者	Li, Ce
作者单位	1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China; 2.Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China; 3.Xi An Jiao Tong Univ, Sch Software Engn, Xian 710049, Peoples R China
第一作者单位	电气工程与信息工程学院
通讯作者单位	电气工程与信息工程学院
第一作者的第一单位	电气工程与信息工程学院
推荐引用方式 GB/T 7714	Li, Ce,Zhang, Dong,Du, Shaoyi,et al. Deformation and Refined Features Based Lesion Detection on Chest X-Ray[J]. IEEE Access,2020,8(8):14675-14689.
APA	Li, Ce,Zhang, Dong,Du, Shaoyi,&Tian, Zhiqiang.(2020).Deformation and Refined Features Based Lesion Detection on Chest X-Ray.IEEE Access,8(8),14675-14689.
MLA	Li, Ce,et al."Deformation and Refined Features Based Lesion Detection on Chest X-Ray".IEEE Access 8.8(2020):14675-14689.

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