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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Citations
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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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