Deep Learning Approaches for Classification of Honeycombing and Ground Glass Opacity in Lung CT Imaging: A Systematic Review
- DOI
- 10.3390/bioengineering13101134
- Published
- 2026-09-28
- Container
- Bioengineering
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/bioengineering13101134,
title = {Deep Learning Approaches for Classification of Honeycombing and Ground Glass Opacity in Lung CT Imaging: A Systematic Review},
author = {Hairul Aysa Abdul Halim Sithiq and Liyana Shuib and Muneer Ahmad and Chermaine Deepa Antony and Mohsin Iftikhar and Peer Azmat Shah},
year = {2026},
journal = {Bioengineering},
doi = {10.3390/bioengineering13101134},
url = {https://doi.org/10.3390/bioengineering13101134}
}RIS
TY - JOUR TI - Deep Learning Approaches for Classification of Honeycombing and Ground Glass Opacity in Lung CT Imaging: A Systematic Review AU - Hairul Aysa Abdul Halim Sithiq AU - Liyana Shuib AU - Muneer Ahmad AU - Chermaine Deepa Antony AU - Mohsin Iftikhar AU - Peer Azmat Shah PY - 2026 JO - Bioengineering DO - 10.3390/bioengineering13101134 UR - https://doi.org/10.3390/bioengineering13101134 ER -
APA
Sithiq, H. A. A. H., Shuib, L., Ahmad, M., Antony, C. D., Iftikhar, M., & Shah, P. A. (2026). Deep Learning Approaches for Classification of Honeycombing and Ground Glass Opacity in Lung CT Imaging: A Systematic Review. Bioengineering. https://doi.org/10.3390/bioengineering13101134
Source records
- crossref · retrieved 2026-11-03T23:10:59.165Z