The DOI matches its Crossref record; 109 of the 109 references it cites were found in a registry or an index.
Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges
Open accessAbstract
Cancer is a fatal illness often caused by genetic disorder aggregation and a variety of pathological changes. Cancerous cells are abnormal areas often growing in any part of human body that are life-threatening. Cancer also known as tumor must be quickly and correctly detected in the initial stage to identify what might be beneficial for its cure. Even though modality has different considerations, such as complicated history, improper diagnostics and treatement that are main causes of deaths. The aim of the research is to analyze, review, categorize and address the current developments of human body cancer detection using machine learning techniques for breast, brain, lung, liver, skin cancer leukemia. The study highlights how cancer diagnosis, cure process is assisted using machine learning with supervised, unsupervised and deep learning techniques. Several state of art techniques are categorized under the same cluster and results are compared on benchmark datasets from accuracy, sensitivity, specificity, false-positive metrics. Finally, challenges are also highlighted for possible future work.
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Cites
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Going deeper with convolutions
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The multimodal brain tumor image segmentation benchmark (BRATS)
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Computational Radiomics System to Decode the Radiographic Phenotype
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Ovarian cancer statistics, 2018
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Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification
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Cited by
Machine learning and deep learning algorithms for skin cancer classification from dermoscopic images
Bechelli, S., & Delhommelle, J. (2022) · Bioengineering · doi:10.3390/bioengineering9030097 · Cited by 181
Melarkode, N., Srinivasan, K., Qaisar, S. M., & Plawiak, P. (2023) · Cancers · doi:10.3390/cancers15041183 · Cited by 144
Machine learning-enabled optimization of extrusion-based 3D printing
Rahmani Dabbagh, S., Ozcan, O., & Tasoglu, S. (2022) · Methods · doi:10.1016/j.ymeth.2022.08.002 · Cited by 94
Metadata
- Authors
- Saba, T.
- Venue
- Journal of Infection and Public Health
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 13
- Issue
- 9
- Pages
- 1274-1289
- ISSN
- 1876-0341 (ISSN-L)
- OpenAlex
- W3046874697
- Cited by
- 284
- References
- 109
- FWCI
- 15.53
- Type
- journal article
- Language
- en
- Open access
- gold
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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.
