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Research Articles

An efficient glioma classification and grade detection using hybrid convolutional neural network-based SVM model

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Pages 1-22 | Received 27 Aug 2022, Accepted 12 May 2023, Published online: 26 Jun 2023

References

  • Maruthamuthu A. Brain tumour segmentation from MRI using superpixels based spectral clustering. Journal of King Saud University - Computer and Information Sciences. 2020;32:1182–1193. doi:10.1016/j.jksuci.2018.01.009
  • Venkataramani V, Tanev DI, Strahle C, et al. Glutamatergic synaptic input to glioma cells drives brain tumour progression. Nature. 2019;573:532–538. doi:10.1038/s41586-019-1564-x
  • Angulakshmi M, Lakshmi Priya GG. Automated brain tumour segmentation techniques- A review. Int J Imaging Syst Technol. 2017;27:66–77. doi:10.1002/ima.22211
  • Shree NV, Kumar TN. Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inform. 2018;5:23–30. doi:10.1007/s40708-017-0075-5
  • El-Dahshan ES, Mohsen HM, Revett K, et al. Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Syst Appl. 2014;41:5526–5545. doi:10.1016/j.eswa.2014.01.021
  • Mohan G, Subashini MM. MRI based medical image analysis: survey on brain tumor grade classification. Biomed Signal Process Control. 2018;39:139–161. doi:10.1016/j.bspc.2017.07.007
  • Florimbi G, Fabelo H, Torti E, et al. Accelerating the K-nearest neighbors filtering algorithm to optimize the real-time classification of human brain tumor in hyperspectral images. Sensors. 2018;18:2314–2333. doi:10.3390/s18072314
  • Fabelo H, Halicek M, Ortega S, et al. Deep learning-based framework for In vivo identification of glioblastoma tumor using hyperspectral images of human brain. Sensors. 2019;19:920–944. doi:10.3390/s19040920
  • Alam MS, Rahman MM, Hossain MA, et al. Automatic human brain tumor detection in MRI image using template-based K means and improved fuzzy C means clustering algorithm. Big Data and Cogn Computing. 2019;3:27–44. doi:10.3390/bdcc3020027
  • Kaldera HN, Gunasekara SR, Dissanayake MB. Brain tumor classification and segmentation using faster R-CNN. IEEE Advances in Science and Engineering Technology International Conferences (ASET). 2019: 1–6.
  • Padma A, Sukanesh R. Automatic classification and segmentation of brain tumor in CT images using optimal dominant gray level run length texture features. Int J Adv Comput Sci Appl. 2011;2(10):53–59.
  • Díaz-Pernas FJ, Martínez-Zarzuela M, Antón-Rodríguez M, et al. A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network. Healthcare Multidiscip Digital Publishing Institute. 2021;9:153–166.
  • Selvapandian A, Athilingam R, Sivakumar P. Performance analysis of glioma brain tumor detection and segmentation using image registration technique. Mater Today Proc. 2020: 1–5.
  • Khosravanian A, Rahmanimanesh M, Keshavarzi P, et al. Fast level set method for glioma brain tumor segmentation based on superpixel fuzzy clustering and lattice Boltzmann method. Comput Methods Programs Biomed. 2021;198:105809–105838. doi:10.1016/j.cmpb.2020.105809
  • Lakshmanaprabu SK, Mohanty SN, Shankar K, et al. Optimal deep learning model for classification of lung cancer on CT images. Future Gener Comput Syst. 2019;92:374–382. doi:10.1016/j.future.2018.10.009
  • Zhou M, Scott J, Chaudhury B, et al. Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. Am J Neuroradiol. 2018;39:208–216. doi:10.3174/ajnr.A5391
  • Khairandish MO, Sharma M, Jain V, et al. A hybrid CNN-SVM threshold segmentation approach for tumor detection and classification of MRI brain images. IRBM. 2021;43.
  • Bacanin N, Zivkovic M, Al-Turjman F, et al. Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application. Sci Rep. 2022;12(1):1–20. doi:10.1038/s41598-022-09744-2
  • Niu XX, Suen CY. A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recognit. 2012;45(4):1318–1325. doi:10.1016/j.patcog.2011.09.021
  • Houssein EH, Hosney ME, Mohamed WM, et al. Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data. Neural Comput Appl. 2022;35:1–25.
  • Selvapandian A, Manivannan K. Fusion based glioma brain tumor detection and segmentation using ANFIS classification. Comput Methods Programs Biomed. 2018;166. doi:10.1016/j.cmpb.2018.09.006
  • Naser MA, Deen MJ. Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images. Comput Biol Med. 2020;121:103758–103765. doi:10.1016/j.compbiomed.2020.103758
  • Soltaninejad M, Yang G, Lambrou T. Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int J Comput Assist Radiol Surg. 2017;12:183–203. doi:10.1007/s11548-016-1483-3.
  • Islam A, Hossain MF, Saha C. A new hybrid approach for brain tumor classification using BWT-KSVM. 2017 4th International Conference on Advances in Electrical Engineering (ICAEE). 2017: 241–246. doi:10.1109/ICAEE.2017.8255360.
  • Ezugwu AE, Agbaje MB, Aljojo N, et al. A comparative performance study of hybrid firefly algorithms for automatic data clustering. IEEE Access. 2020;8:121089–121118. doi:10.1109/access.2020.3006173
  • Anitha R, Siva Sundhara Raja D. Development of computer-aided approach for brain tumor detection using random forest classifier. Int J Imaging Syst Technol. 2018;28:48–53. doi:10.1002/ima.22255
  • Houssein EH, Hosney ME, Oliva D, et al. A novel hybrid Harris hawks optimization and support vector machines for drug design and discovery. Comput Chem Eng. 2020;133:106656–106697. doi:10.1016/j.compchemeng.2019.106656
  • Khan A, Khan A, Bangash JI, et al. Cuckoo search-based SVM (CS-SVM) model for real-time indoor position estimation in IoT networks. Security and Communication Networks. 2021;2021:1–7. doi:10.1155/2021/6654926
  • Anaraki AK, Ayati M, Kazemi F. Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybernetics and Biomedical Engineering. 2019;39:63–74. doi:10.1016/j.bbe.2018.10.004
  • Sultan HH, Salem NM, Al-Atabany W. Multi-classification of brain tumor images using deep neural network. IEEE Access. 2019;7:69215–69225. doi:10.1109/access.2019.2919122
  • Maqsood S, Damasevicius R, Shah FM. An efficient approach for the detection of brain tumors using fuzzy logic and U-NET CNN classification. In International Conference on Computational Science and Its Applications (pp. 105–118). Springer, Cham. 2021.
  • Kadry S, Damaševičius R, Taniar D, et al. Extraction of tumor in breast MRI using joint thresholding and segmentation–A study. In 2021 Seventh International Conference on Bio Signals, Images, and Instrumentation (ICBSII) (pp. 1–5). IEEE. 2021.
  • Rajinikanth V, Kadry S, Nam Y. Convolutional-Neural-Network assisted segmentation and SVM classification of brain tumor in clinical MRI slices. Inf Technol Control. 2021;50(2):342–356. doi:10.5755/j01.itc.50.2.28087
  • Maqsood S, Damaševičius R, Maskeliūnas R. Multi-modal brain tumor detection using deep neural network and multiclass SVM. Medicina (B Aires). 2022;58(8):1090–1108. doi:10.3390/medicina58081090
  • Bezdan T, Milosevic S, Venkatachalam K, et al. Optimizing convolutional neural network by hybridized elephant herding optimization algorithm for magnetic resonance image classification of glioma brain tumor grade. In 2021 Zooming Innovation in Consumer Technologies Conference (ZINC) (pp. 171–176). IEEE. 2021.
  • Kesav N, Jibukumar MG. Efficient and low complex architecture for detection and classification of brain tumor using RCNN with two channel CNN. Journal of King Saud University - Computer and Information Sciences. 2022;34(8):6229–6242. doi:10.1016/j.jksuci.2021.05.008
  • Bezdan T, Zivkovic M, Tuba E, et al. Glioma brain tumor grade classification from mri using convolutional neural networks designed by modified fa. In International conference on intelligent and fuzzy systems (pp. 955–963). Springer, Cham. 2020.
  • Angelin Jeba J, Nirmala Devi S, Meena M. Modified CNN architecture for efficient classification of glioma brain tumour. IETE J Res. 2022;68:1–14. doi:10.1080/03772063.2022.2101553
  • Gull S, Akbar S, Khan HU. Automated detection of brain tumor through magnetic resonance images using convolutional neural network. Biomed Res Int. 2021;2021:1–14.
  • Brats Dataset. https://www.med.upenn.edu/sbia/brats2017/data.html.
  • ACRIN-FMISO-Brain (ACRIN-6684). Dataset: https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=33948305.
  • Amin J, Sharif M, Haldorai A, et al. Brain tumor detection and classification using machine learning: a comprehensive survey. Complex & Intelligent Systems. 2021;8:1–23.
  • Chen T, Xiao F, Yu Z, et al. Detection and grading of gliomas using a novel two-phase machine learning method based on mri images. Front Neurosci. 2021;15:1–10. doi:10.3389/fnins.2021.650629
  • Ong HC, Tilahun SL, Lee WS, et al. Comparative study of prey-predator algorithm and firefly algorithm. Intelligent Automation & Soft Computing. 2017;23:1–8.
  • Zacharaki EI, Wang S, Chawla S, et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med. 2009;62(6):1609–1618. doi:10.1002/mrm.22147
  • Ruiz-Gonzalez R, Gomez-Gil J, Gomez-Gil FJ, et al. An SVM-based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis. Sensors. 2014;14(11):20713–20735. doi:10.3390/s141120713

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