The DOI matches its Crossref record.
Lung cancer classification using neural networks for CT images
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Lung Nodule Classification Using Deep Features in CT Images
Kumar, D., Wong, A., & Clausi, D. A. (2015) · 2015 12th Conference on Computer and Robot Vision · doi:10.1109/crv.2015.25 · Cited by 398
Gong, Y., Zhang, Y., Zhu, H., Lv, J., Cheng, Q., Zhang, H., He, Y., & Wang, S. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2946059 · Cited by 101
RLDS: An explainable residual learning diagnosis system for fetal congenital heart disease
Qiao, S., Pang, S., Luo, G., Pan, S., Yu, Z., Chen, T., & Lv, Z. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2021.10.001 · Cited by 42
Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea
Brennan, H. L., & Kirby, S. D. (2022) · Journal of Otolaryngology - Head & Neck Surgery · doi:10.1186/s40463-022-00566-w · Cited by 39
“AI is currently being employed in various medical settings, including detecting ischemia in myocardial perfusion imaging, pulmonary embolism diagnosis, and classification of lung cancer with computerized tomography (CT) images [30–32].”
MethodYang, Y., Chen, H., Wang, D., Luo, W., Zhu, B., & Zhang, Z. (2014) · Chinese Medical Journal · doi:10.3760/cma.j.issn.0366-6999.20133101 · Cited by 26
Metadata
- Authors
- Kuruvilla, J., & Gunavathi, K.
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 113
- Issue
- 1
- Pages
- 202-209
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W2136177244
- Cited by
- 366
- References
- 0
- FWCI
- 18.41
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Lung cancer classification using neural networks for CT images. https://app.cochat.ai/papers/10.1016/j.cmpb.2013.10.011
- The paper
- Kuruvilla, J., & Gunavathi, K. (2014). Lung cancer classification using neural networks for CT images. Computer Methods and Programs in Biomedicine. https://doi.org/10.1016/j.cmpb.2013.10.011
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
