FVC-NET: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning.
- DOI
- 10.1155/2022/2832400
- Published
- 2022
- Container
- Computational intelligence and neuroscience
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1155/2022/2832400,
title = {FVC-NET: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning.},
author = {Yadav A and Saxena R and Kumar A and Walia TS and Zaguia A and Kamal SMM},
year = {2022},
journal = {Computational intelligence and neuroscience},
doi = {10.1155/2022/2832400},
url = {https://doi.org/10.1155/2022/2832400}
}RIS
TY - JOUR TI - FVC-NET: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning. AU - Yadav A AU - Saxena R AU - Kumar A AU - Walia TS AU - Zaguia A AU - Kamal SMM PY - 2022 JO - Computational intelligence and neuroscience DO - 10.1155/2022/2832400 UR - https://doi.org/10.1155/2022/2832400 ER -
APA
A, Y., R, S., A, K., TS, W., A, Z., & SMM, K. (2022). FVC-NET: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning.. Computational intelligence and neuroscience. https://doi.org/10.1155/2022/2832400
Source records
- pubmed · retrieved 2026-09-25T07:49:27.364Z