The DOI matches its Crossref record; 220 of the 220 references it cites were found in a registry or an index.
Brain tumor detection and classification using machine learning: a comprehensive survey
Open accessAbstract
Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.
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1 retracted
Umer, M. J., Amin, J., Sharif, M. I., Anjum, M. A., Azam, F., & Shah, J. H. (2021) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6434 · Cited by 42
Self-citationRetractedComputed Tomography — An Increasing Source of Radiation Exposure
Brenner, D. J., & Hall, E. J. (2007) · New England Journal of Medicine · doi:10.1056/nejmra072149 · Cited by 8,748
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781
Yang (2021) · doi:10.1038/s41598-020-79139-8 · Cited by 5,035
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498
A survey of medical image registration
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Brain tumor segmentation with Deep Neural Networks
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.05.004 · Cited by 3,278
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538465 · Cited by 2,735
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Cited by
Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview
Solanki, S., Singh, U. P., Chouhan, S. S., & Jain, S. (2023) · IEEE Access · doi:10.1109/access.2023.3242666 · Cited by 259
Enhancing brain tumor detection in MRI images using YOLO-NeuroBoost model
Chen, A., Lin, D., & Gao, Q. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1445882 · Cited by 35
Metadata
- Authors
- Amin, J., Sharif, M., Haldorai, A., Yasmin, M., & Nayak, R. S.
- Venue
- Complex & Intelligent Systems
- Published
- 2021-11-08
- Volume
- 8
- Issue
- 4
- Pages
- 3161-3183
- ISSN
- 2198-6053 (ISSN-L)
- 2199-4536
- OpenAlex
- W3211740276
- Cited by
- 503
- References
- 220
- FWCI
- 22.56
- Type
- journal article
- Language
- en
- Open access
- gold
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