Improved image-based lung opacity detection of VGG16 model

Ziheng Li; Yuelong Zhang; Jiankai Zuo; Yupeng Zou; Mingxuan Song

doi:10.1117/12.2604522

5 October 2021 Improved image-based lung opacity detection of VGG16 model

Ziheng Li, Yuelong Zhang, Jiankai Zuo, Yupeng Zou, Mingxuan Song

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 119110P (2021) https://doi.org/10.1117/12.2604522
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

In order to quickly and effectively detect lung information in different medical images, this paper designs an improved VGG16 image-based lung opacity classification detection method based on deep transfer learning. This paper applies offline data enhancement technology to increase the number of samples, improves VGG, and employs transfer learning to train the lung recognition model. The results show that the improved VGG16 network has an accuracy rate of 85% for the classification and recognition of lung pictures, and can accurately detect lung pathological mutation information.

Citation Download Citation

Ziheng Li, Yuelong Zhang, Jiankai Zuo, Yupeng Zou, and Mingxuan Song "Improved image-based lung opacity detection of VGG16 model", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 119110P (5 October 2021); https://doi.org/10.1117/12.2604522

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