Lung Nodule Classification Based on 3D Convolutional Neural Network
In order to improve the classification accuracy of ground glass nodules that are difficult to segment and diagnose and at the same time，the VGG16 network structure has deep convolutional layers and many parameters，A 3D deep convolutional neural network based on intensity，texture，and shape-enhanced i...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2021-08-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1997 |
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| Summary: | In order to improve the classification accuracy of ground glass nodules that are difficult to segment and diagnose and at the same time，the VGG16 network structure has deep convolutional layers and many parameters，A 3D deep convolutional neural network based on intensity，texture，and shape-enhanced images for pulmonary nodule recognition was proposed. The VGG16 network structure was optimized，and the proposed model was trained and tested on the public nodule dataset of lung nodules LIDC-IDＲI. The results showed that the proposed method using the composition of intensity，texture and shape-enhanced has the highest image classification accuracy，with an accuracy of 91. 7% . Other measures，including sensitivity and specificity，also improved slightly，It is superior to existing methods. |
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| ISSN: | 1007-2683 |