Paper
5 October 2021 Improved image-based lung opacity detection of VGG16 model
Author Affiliations +
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.
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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
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KEYWORDS
Lung

Convolutional neural networks

Data modeling

Image classification

Opacity

Convolution

Image segmentation

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