The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Deep learning for image-based cancer detection and diagnosis − A survey
Abstract
Abstract In this paper, we aim to provide a survey on the applications of deep learning for cancer detection and diagnosis and hope to provide an overview of the progress in this field. In the survey, we firstly provide an overview on deep learning and the popular architectures used for cancer detection and diagnosis. Especially we present four popular deep learning architectures, including convolutional neural networks, fully convolutional networks, auto-encoders, and deep belief networks in the survey. Secondly, we provide a survey on the studies exploiting deep learning for cancer detection and diagnosis. The surveys in this part are organized based on the types of cancers. Thirdly, we provide a summary and comments on the recent work on the applications of deep learning to cancer detection and diagnosis and propose some future research directions.
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Cites
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Dermatologist-level classification of skin cancer with deep neural networks
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Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
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The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
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Cited by
Deep Learning for Time Series Forecasting: A Survey
Torres, J. F., Hadjout, D., Sebaa, A., Martínez-Álvarez, F., & Troncoso, A. (2021) · Big Data · doi:10.1089/big.2020.0159 · Cited by 818
Automated pulmonary nodule detection in CT images using deep convolutional neural networks
Xie, H., Yang, D., Sun, N., Chen, Z., & Zhang, Y. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.031 · Cited by 459
Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review
Houssein, E. H., Emam, M. M., Ali, A. A., & Suganthan, P. N. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114161 · Cited by 404
A survey on deep learning in medicine: Why, how and when?
Piccialli, F., Somma, V. D., Giampaolo, F., Cuomo, S., & Fortino, G. (2021) · Information Fusion · doi:10.1016/j.inffus.2020.09.006 · Cited by 348
Cervical cancer classification using convolutional neural networks and extreme learning machines
Ghoneim, A., Muhammad, G., & Hossain, M. S. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2019.09.015 · Cited by 312
Metadata
- Authors
- Hu, Z., Tang, J., Wang, Z., Zhang, K., Zhang, L., & Sun, Q.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 83
- Pages
- 134-149
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2804383999
- Cited by
- 509
- References
- 22
- FWCI
- 32.39
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hu, Z., Tang, J., Wang, Z., Zhang, K., Zhang, L., & Sun, Q. (2018). Deep learning for image-based cancer detection and diagnosis − A survey. Pattern Recognition. https://doi.org/10.1016/j.patcog.2018.05.014
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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.
