Optimized Lung Nodule Prediction Model for Lung Cancer Using Contour Features Extraction-Reference-Cited by-同舟云学术

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Optimized Lung Nodule Prediction Model for Lung Cancer Using Contour Features Extraction

Published:2023-06 Issue:2 Volume:32 Page:126-136
ISSN:1060-992X
Container-title:Optical Memory and Neural Networks
language:en
Short-container-title:Opt. Mem. Neural Networks

Author:

Faiyaz Ahmad ,Hariharan U.,Karthick S.,Pawar Vaibhav Eknath,Sharon Priya S.

Publisher

Allerton Press

Subject

Electrical and Electronic Engineering,General Computer Science,Electronic, Optical and Magnetic Materials

Link

https://link.springer.com/content/pdf/10.3103/S1060992X23020091.pdf

Reference17 articles.

1. Peng, T., Xu, T.C., Wang, Y., Zhou, H., Candemir, S., Zaki, W.M.D.W., Ruan, S.J., Wang, J., and Chen, X., Hybrid automatic lung segmentation on chest CT scans, IEEE Access, 2020, vol. 8, pp. 73293–73306.

2. Chen, Y., Wang, Y., Hu, F., and Wang, D., A Lung dense deep convolution Neural Network for robust lung parenchyma segmentation, IEEE Access, 2020, vol. 8, pp. 93527–93547.

3. Qiu, S., Sun, J., Zhou, T., Gao, G., He, Z., and Liang, T., Spiculation sign recognition in a pulmonary nodule based on spiking Neural P systems, BioMed Res. Int., 2020, vol. 2020.

4. Huang, W. and Hu, L., Using a noisy U-net for detecting lung nodule candidates, IEEE Access, 2019, vol. 7, pp. 67905–67915.

5. Anthimopoulos, M., Christodoulidis, S., Ebner, L., Geiser, T., Christe, A., and Mougiakakou, S., Semantic segmentation of pathological lung tissue with dilated fully convolutional networks, IEEE J. Biomed. Health Inf., 2018, vol. 23, no. 2, pp. 714–722.

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