Mr. Fan Wei Kang Profile

Fan Wei Kang

at Zhengzhou Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 9 August 2018 Paper

A modified faster R-CNN method to improve the performance of the pulmonary nodule detection

Weikang Fan, Huiqin Jiang, Ling Ma, Jianbo Gao, Haojin Yang

Proceedings Volume 10806, 108065A (2018) https://doi.org/10.1117/12.2502893

KEYWORDS: Lung, Convolution, Lung cancer, Data modeling, Computed tomography, Chest, Feature extraction, Signal attenuation, Neural networks, Deconvolution

Read Abstract +

In order to realize the accurate and quick positioning of pulmonary nodules in hundreds of two-dimensional CT chest images and reduce the burden of radiologist, the paper proposes a modified faster R-CNN method to improve the performance of the pulmonary nodule detection. Firstly, data enhancement technology is adopted to expand the dataset. Secondly, the image is fed into VGG-16 with de-convolution to extract the shared convolution features. Then, the shared convolution feature is sent to the region proposal network (RPN) to output candidate lung nodule region. Finally, the candidate lung nodule region and the previous shared convolution features are input into ROI pooling layer at the same time, and the characteristics of the corresponding candidate area are extracted. Through the connection layer, a multi task classifier is used to position the regression of the candidate region. According to the features of complex chest image background, large detecting object range and relatively small size of pulmonary nodule compared with natural objects, we design a smaller anchor box to accommodate changes in lung nodule size. In order to get the more accurate description of the characteristics of pulmonary nodules, we add a de-convolution layer with 4, 4, 2 and 512 for nuclear size, step size, filling size and number of nuclei respectively after the last layer of VGG-16 network conv5_3 , resulting in a higher de-convolution feature resolution. Finer granularity can be restored compared with the original feature map. The experimental results show that the average detection accuracy is up by 6.9 percentage points compared with the original model. This model can well detect solitary pulmonary nodules and pulmonary nodules and small nodules, showing certain clinical significance for early screening of lung cancer.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years