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Design of Lung Nodules Segmentation and Recognition Algorithm Based on Deep Learning by Yu, Hui (author);Li, Jinqiu (author);Zhang, Lixin (author);Cao, Yuzhen (author);Yu, Xuyao (author);Sun, Jinglai (author) is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
What is Design of Lung Nodules Segmentation and Recognition Algorithm Based on Deep Learning about?
## Abstract ## Background Accurate segmentation and recognition algorithm of lung nodules has great important value of reference for early diagnosis of lung cancer. An algorithm is proposed for 3D CT sequence images in this paper based on 3D Res U-Net segmentation network and 3D ResNet50 classification network. The common convolutional layers in encoding and decoding paths of U-Net are replaced by residual units while the loss function is changed to Dice loss after using cross entropy loss to accelerate network convergence. Since the lung nodules are small and rich in 3D information, the ResNet50 is improved by replacing the 2D convolutional layers with 3D convolutional layers and reducing the sizes of some convolution kernels, 3D ResNet50 network is obtained for the diagnosis of benign and malignant lung nodules. ## Results 3D Res U-Net was trained and tested on 1044 CT subcases in the LIDC-IDRI database. The segmentation result shows that the Dice coefficient of 3D Res U-Net is above 0.8 for the segmentation of lung nodules larger than 10 mm in diameter. 3D ResNet50 was trained and tested on 2960 lung nodules in the LIDC-IDRI database. The classification result shows that the dia
Who reads Design of Lung Nodules Segmentation and Recognition Algorithm Based on Deep Learning?
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Yu, Hui (author);Li, Jinqiu (author);Zhang, Lixin (author);Cao, Yuzhen (author);Yu, Xuyao (author);Sun, Jinglai (author)
- Publisher
- Springer Science and Business Media LLC
- Published
- 2021
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)