Discrimination of benign and malignant pulmonary tumors in computed tomography: effective priori information of fast learning network architecture

Hao-Jen Wang; Leng-Rong Chen; Li-Wei Chen; Yi-Chang Chen; Shun-Mao Yang; Mong-Wei Lin; Joseph Chang; Chia-Chen Li; Chia-Yen Lee; Jin-Shing Chen; Yeun-Chung Chang; Chung-Ming Chen

doi:10.1117/12.2512846

15 March 2019 Discrimination of benign and malignant pulmonary tumors in computed tomography: effective priori information of fast learning network architecture

Hao-Jen Wang, Leng-Rong Chen, Li-Wei Chen, Yi-Chang Chen, Shun-Mao Yang, Mong-Wei Lin, Joseph Chang, Chia-Chen Li, Chia-Yen Lee, Jin-Shing Chen, Yeun-Chung Chang, Chung-Ming Chen

Author Affiliations +

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 109493B (2019) https://doi.org/10.1117/12.2512846
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

This study explores the influence of prior information for deep learning networks to discriminate the benign and malignant of pulmonary tumors in computed tomography. In this study, because the number of nodule samples is sparse, this study proposes the concept of Multiple-Window to provide prior knowledge for Convolutional neural network (CNN). In the Multiple-Window CNN, we use the 5 windows including lung window, abdomen window, bone window, and chest window to generate the nodule sample. The sparse number of nodule samples, through the characteristics of the CT image dynamic range, make more prior information in a limited amount of data. The results show that the increase of suitably prior information (window channel) be included, CNN performance has improved. When the input is original dicom image, the accuracy of CNN is 0.82, sensitivity is 0.82, and specificity is 0.82. When the input is 4 kinds channel of window type, the accuracy is 0.9, sensitivity is 0.84, and specificity is 0.96.

Citation Download Citation

Hao-Jen Wang, Leng-Rong Chen, Li-Wei Chen, Yi-Chang Chen, Shun-Mao Yang, Mong-Wei Lin, Joseph Chang, Chia-Chen Li, Chia-Yen Lee, Jin-Shing Chen, Yeun-Chung Chang, and Chung-Ming Chen "Discrimination of benign and malignant pulmonary tumors in computed tomography: effective priori information of fast learning network architecture ", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109493B (15 March 2019); https://doi.org/10.1117/12.2512846

ACCESS THE FULL ARTICLE

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: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Computed tomography

Lung

Convolutional neural networks

Abdomen

Bone

Chest

Lung cancer

Show All Keywords

Keywords/Phrases

Search In:

Publication Years