Ms. Yeseul Choi Profile

Yeseul Choi

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 | 16 March 2020 Paper

Performance investigation of deep learning vs. classifier for polyp differentiation via texture features

David Liang, David Wang, Alice Wei, Yeseul Choi, Shu Zhang, Marc Pomeroy, Perry Pickhardt

Proceedings Volume 11314, 113143B (2020) https://doi.org/10.1117/12.2550007

KEYWORDS: Data modeling, Colorectal cancer, Computer aided diagnosis and therapy, Feature extraction, Cancer, Performance modeling, Computed tomography, 3D modeling, Image classification, Colon

Read Abstract +

Computer-aided diagnosis (CADx) of polyps is essential for advancing computed tomography colonography (CTC) with diagnostic capability. In this paper, we present a study of investigating the performance between deep learning and Random Forest (RF) classifier for polyp differentiation in CTC. First, we conducted feature extraction via an extended Haralick model (eHM) to build a total of 30 texture features. The gray level co-occurrence matrix (GLCM) is generated to encode 3D CT image information into a 2D matrix as input to the convolutional neural network (CNN). Then, we split the polyp classification into two state-of-the-art frameworks: the eHM texture features/RF and the GLCM texture matrices/CNN. We evaluated their performances by the merit of area under the curve of receiver operating characteristic using 1,278 polyps (confirmed by pathology). Results demonstrated that by balancing the data, both CNN model and RF classifier can learn or analyze features effectively, and achieve high performance. RF classifier in general outperformed CNN model with a gain of 6.4% (balanced datasets) and 5.4% (unbalanced datasets), showing its effective in feature extraction and analysis for polyp differentiation. However, the performance of CNN got improved through the addition of new data with a gain of 3.6% (balanced datasets) and 3.4% (unbalanced datasets), whereas RF classifier showed no gain when we enlarged datasets. This demonstrated that CNN model have the potential to improve the classification task performance when dealing with larger dataset. This study provided valuable information on how to design experiments to improve CADx of polyps.

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