The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Lung cancer cell identification based on artificial neural network ensembles
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Citations
Cites
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,081
The Strength of Weak Learnability
Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600
Hansen, L. K., & Salamon, P. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.58871 · Cited by 4,260
Popular Ensemble Methods: An Empirical Study
Opitz, D., & Maclin, R. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.614 · Cited by 2,953
Neural Network Ensembles, Cross Validation, and Active Learning
Krogh, A., & Vedelsby, J. (1994) · Cited by 1,838
Ensemble learning via negative correlation
Liu, Y., & Yao, X. (1999) · Neural Networks · doi:10.1016/s0893-6080(99)00073-8 · Cited by 712
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
SHARKEY, A. J. C. (1999) · Cited by 482
Learning with Ensembles: How over-fitting can be useful
Peter Sollich, A., & Krogh, A. (1995) · Cited by 353
Cited by
Ensembling neural networks: Many could be better than all
Zhou, Z.-H., Wu, J., & Tang, W. (2002) · Artificial Intelligence · doi:10.1016/s0004-3702(02)00190-x · Cited by 1,964
Self-citationThe responsibility gap: Ascribing responsibility for the actions of learning automata
Matthias, A. (2004) · Ethics and Information Technology · doi:10.1007/s10676-004-3422-1 · Cited by 1,101
Could Machine Learning Break the Convection Parameterization Deadlock?
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., & Yacalis, G. (2018) · Geophysical Research Letters · doi:10.1029/2018gl078202 · Cited by 497
Deep learning for lung Cancer detection and classification
Asuntha, A., & Srinivasan, A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08394-3 · Cited by 382
Physician perspectives on integration of artificial intelligence into diagnostic pathology
Sarwar, S., Dent, A., Faust, K., Richer, M., Djuric, U., Van Ommeren, R., & Diamandis, P. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0106-0 · Cited by 297
Metadata
- Authors
- Zhou, Z.-H., Jiang, Y., Yang, Y.-B., & Chen, S.-F.
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2002-01
- Volume
- 24
- Issue
- 1
- Pages
- 25-36
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W2144012133
- Cited by
- 355
- References
- 25
- FWCI
- 11.71
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Lung cancer cell identification based on artificial neural network ensembles. https://app.cochat.ai/papers/10.1016/s0933-3657(01)00094-x
- The paper
- Zhou, Z.-H., Jiang, Y., Yang, Y.-B., & Chen, S.-F. (2002). Lung cancer cell identification based on artificial neural network ensembles. Artificial Intelligence in Medicine. https://doi.org/10.1016/s0933-3657(01)00094-x
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.
