The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Cervical cancer classification using convolutional neural networks and extreme learning machines
Abstract
Abstract Cervical cancer is one of the main reasons of death from cancer in women. The complication of this cancer can be limited if it is diagnosed and treated at an early stage. In this paper, we propose a cervical cancer cell detection and classification system based on convolutional neural networks (CNNs). The cell images are fed into a CNNs model to extract deep-learned features. Then, an extreme learning machine (ELM)-based classifier classifies the input images. CNNs model is used via transfer learning and fine tuning. Alternatives to the ELM, multi-layer perceptron (MLP) and autoencoder (AE)-based classifiers are also investigated. Experiments are performed using the Herlev database. The proposed CNN-ELM-based system achieved 99.5% accuracy in the detection problem (2-class) and 91.2% in the classification problem (7-class).
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Cites
Extreme learning machine: Theory and applications
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks
Yu, L., Chen, H., Dou, Q., Qin, J., & Heng, P.-A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2642839 · Cited by 1,140
AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images
Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S., & Navab, N. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528120 · Cited by 606
Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion
Amin, S. U., Alsulaiman, M., Muhammad, G., Mekhtiche, M. A., & Shamim Hossain, M. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.06.027 · Cited by 523
Self-citationDeep learning for image-based cancer detection and diagnosis − A survey
Hu, Z., Tang, J., Wang, Z., Zhang, K., Zhang, L., & Sun, Q. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.05.014 · Cited by 509
Emotion recognition using deep learning approach from audio–visual emotional big data
Hossain, M. S., & Muhammad, G. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.008 · Cited by 453
Self-citationInternational Conference on Pattern Recognition
Bouchrika, I., Carter, J. N., Nixon, M. S., Mörzinger, R., & Thallinger, G. (2010) · ePrints Soton (University of Southampton) · Cited by 447
Deep Feature Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network
Chen, M., Shi, X., Zhang, Y., Wu, D., & Guizani, M. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2017.2717439 · Cited by 443
Applying Deep Learning for Epilepsy Seizure Detection and Brain Mapping Visualization
Hossain, M. S., Amin, S. U., Alsulaiman, M., & Muhammad, G. (2019) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3241056 · Cited by 311
Self-citation
Cited by
Deep learning in selected cancers’ image analysis—a survey
Debelee, T. G., Kebede, S. R., Schwenker, F., & Shewarega, Z. M. (2020) · Journal of Imaging · doi:10.3390/jimaging6110121 · Cited by 70
Metadata
- Authors
- Ghoneim, A., Muhammad, G., & Hossain, M. S.
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 102
- Pages
- 643-649
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W2972574169
- Cited by
- 312
- References
- 38
- FWCI
- 14.74
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
