The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
A distinctive approach in brain tumor detection and classification using MRI
Abstract
Abstract A very exigent task for radiologists is early brain tumor detection. Brain tumor raises very fast, its average size doubles in just twenty-five days. If not treated properly, the survival rate of the patient is normally not more than half a year. It can rapidly lead to death. For this reason, an automatic system is required for brain tumor detection at an early stage. In this paper, an automated method is proposed to easily differentiate between cancerous and non-cancerous Magnetic Resonance Imaging (MRI) of the brain. Different techniques have been applied for the segmentation of candidate lesion. Then a features set is chosen for every applicant lesion using shape, texture, and intensity. At that point, Support Vector Machine (SVM) classifier is applied with different cross validations on the features set to compare the precision of proposed framework. The proposed method is validated on three benchmark datasets such as Harvard, RIDER and Local. The method achieved average 97.1% accuracy, 0.98 area under curve, 91.9% sensitivity and 98.0% specificity. It can be used to identify the tumor more accurately in less processing time as compared to existing methods.
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Cites
The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary
Louis, D. N., Perry, A., Reifenberger, G., von Deimling, A., Figarella-Branger, D., Cavenee, W. K., Ohgaki, H., Wiestler, O. D., Kleihues, P., & Ellison, D. W. (2016) · Acta Neuropathologica · doi:10.1007/s00401-016-1545-1 · Cited by 15,657
The 2007 WHO Classification of Tumours of the Central Nervous System
Louis, D. N., Ohgaki, H., Wiestler, O. D., Cavenee, W. K., Burger, P. C., Jouvet, A., Scheithauer, B. W., & Kleihues, P. (2007) · Acta Neuropathologica · doi:10.1007/s00401-007-0243-4 · Cited by 15,397
Brain tumor segmentation based on a hybrid clustering technique
Abdel-Maksoud, E., Elmogy, M., & Al-Awadi, R. (2015) · Egyptian Informatics Journal · doi:10.1016/j.eij.2015.01.003 · Cited by 421
Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features
Nabizadeh, N., & Kubat, M. (2015) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2015.02.007 · Cited by 261
Sehgal, V., Delproposto, Z., Haddar, D., Haacke, E. M., Sloan, A. E., Zamorano, L. J., Barger, G., Hu, J., Xu, Y., Prabhakaran, K. P., et al. (2006) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.20598 · Cited by 204
[18F]3′-deoxy-3′-fluorothymidine PET for the diagnosis and grading of brain tumors
Choi, S. J., Kim, J. S., Kim, J. H., Oh, S. J., Lee, J. G., Kim, C. J., Ra, Y. S., Yeo, J. S., Ryu, J. S., & Moon, D. H. (2005) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-004-1742-3 · Cited by 189
Logeswari, T., & Karnan, M. (2010) · International Journal of Computer Theory and Engineering · doi:10.7763/ijcte.2010.v2.207 · Cited by 155
Fractal-based brain tumor detection in multimodal MRI
Iftekharuddin, K. M., Zheng, J., Islam, M. A., & Ogg, R. J. (2008) · Applied Mathematics and Computation · doi:10.1016/j.amc.2007.10.063 · Cited by 151
Sudharani, K., Sarma, T. C., & Prasad, K. S. (2016) · Procedia Technology · doi:10.1016/j.protcy.2016.05.153 · Cited by 61
Nabizadeh, N., John, N., & Wright, C. B. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.06.043 · Cited by 53
Cited by
Brain tumor detection using fusion of hand crafted and deep learning features
Saba, T., Sameh Mohamed, A., El-Affendi, M., Amin, J., & Sharif, M. (2020) · Cognitive Systems Research · doi:10.1016/j.cogsys.2019.09.007 · Cited by 433
Self-citationTiwari, A., Srivastava, S., & Pant, M. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.020 · Cited by 361
Big data analysis for brain tumor detection: Deep convolutional neural networks
Amin, J., Sharif, M., Yasmin, M., & Fernandes, S. L. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.04.065 · Cited by 327
Self-citationA decision support system for multimodal brain tumor classification using deep learning
Sharif, M. I., Khan, M. A., Alhussein, M., Aurangzeb, K., & Raza, M. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00321-0 · Cited by 305
Self-citationBrain 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
Metadata
- Authors
- Amin, J., Sharif, M., Yasmin, M., & Fernandes, S. L.
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 139
- Pages
- 118-127
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W2765490497
- Cited by
- 499
- References
- 13
- FWCI
- 8.50
- Type
- journal article
- Language
- en
- Open access
- closed
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