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
	Ce Li 1; Dong Zhang 1; Shaoyi Du 2; Zhiqiang Tian 2
刊名	IEEE Access
	2020-01
卷号	8 期号:8 页码:14675-14689
关键词	Convolution Convolutional neural networks Deep neural networks Deformation Signal detection
ISSN号	2169-3536
DOI	10.1109/ACCESS.2020.2963926
英文摘要	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.
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WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc., United States
WOS记录号	WOS:000524736700027
内容类型	期刊论文
源URL	[http://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	Ce Li
作者单位	1.Lanzhou University of Technology 2.Xi'an Jiaotong University
推荐引用方式 GB/T 7714	Ce Li,Dong Zhang,Shaoyi Du,et al. Deformation and Refined Features Based Lesion Detection on Chest X-Ray[J]. IEEE Access,2020,8(8):14675-14689.
APA	Ce Li,Dong Zhang,Shaoyi Du,&Zhiqiang Tian.(2020).Deformation and Refined Features Based Lesion Detection on Chest X-Ray.IEEE Access,8(8),14675-14689.
MLA	Ce Li,et al."Deformation and Refined Features Based Lesion Detection on Chest X-Ray".IEEE Access 8.8(2020):14675-14689.