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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 11
3511-3515

Pulmonary nodule detection based on improved Faster R-CNN and 3D DCNN

Hu Xinying
Chen Shuyue
Jiao Zhuqing
School of Information Science & Engineering, Changzhou University, Changzhou Jiangsu 213164, China
DOI: 10.19734/j.issn.1001-3695.2018.07.0582

Abstract

Aiming at the low accuracy of traditional lung nodule detection and the high false positive, this paper proposed an improved Faster R-CNN network and improved 3D DCNN to detect candidate nodules and remove false positives. Considering the shape and size of the nodule, etc., the method changed the anchor point number and size to detect the robustness of the nodule on Faster R-CNN, and added a deconvolution layer in the last layer of the feature extractor. In addition, according to the size of the nodule, the method added a small sliding network to enable the network to adaptively generate the region of interest to obtain candidate nodules. In order to remove false positive nodules, it adjusted the convolution kernel parameters based on the 2D DCNN, used the time dimension to generate the 3D DCNN, and applied the Adam algorithm to adjust the network learning rate to change the network weight. The enhancement strategy further extracted the global features of the nodule. The experimental results on the LIDC-IDRI dataset show that the proposed algorithm has an average detection accuracy of 97.71%, and reduces the rate of misdiagnosis and missed diagnosis.

Foundation Support

国家自然科学基金资助项目(51307010)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.07.0582
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 11
Section: Technology of Graphic & Image
Pages: 3511-3515
Serial Number: 1001-3695(2019)11-071-3511-05

Publish History

[2019-11-05] Printed Article

Cite This Article

胡新颖, 陈树越, 焦竹青. 基于改进Faster R-CNN和3D DCNN的肺结节检测算法 [J]. 计算机应用研究, 2019, 36 (11): 3511-3515. (Hu Xinying, Chen Shuyue, Jiao Zhuqing. Pulmonary nodule detection based on improved Faster R-CNN and 3D DCNN [J]. Application Research of Computers, 2019, 36 (11): 3511-3515. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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