Evolutionary gravitational neocognitron neural network optimized with marine predators optimization algorithm for MRI brain tumor classification

References

• Abdel-Basset, M., D. El-Shahat, R. K. Chakrabortty, and M. Ryan. 2021. Parameter estimation of photovoltaic models using an improved marine predators algorithm. Energ. Convers. Manage. 227:113491. doi:10.1016/j.enconman.2020.113491.
   Web of Science ®Google Scholar
• Al-Qazzaz, S., X. Sun, H. Yang, Y. Yang, R. Xu, L. Nokes, and X. Yang. 2021. Image classification-based brain tumour tissue segmentation. Multimedia Tools Appl. 80:993–1008. doi:10.1007/s11042-020-09661-4.
   Web of Science ®Google Scholar
• Arbane, M., R. Benlamri, Y. Brik, and M. Djerioui 2021. Transfer learning for automatic brain tumor classification using MRI images. In 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH), Boumerdes, Algeria, 210–14. IEEE.
   Google Scholar
• Bhatele, K. R., and S. S. Bhadauria. 2020. Brain structural disorders detection and classification approaches: A review. Artif. Intell. Rev. 53:3349–401. doi:10.1007/s10462-019-09766-9.
   Web of Science ®Google Scholar
• Chen, J., G. Yang, H. Khan, H. Zhang, Y. Zhang, S. Zhao, R. Mohiaddin, T. Wong, D. Firmin, and J. Keegan. 2021. JAS-GAN: Generative adversarial network based joint atrium and scar segmentations on unbalanced atrial targets. IEEE J. Biomed. Health Inform. 26:103–14. doi:10.1109/JBHI.2021.3077469.
   Web of Science ®Google Scholar
• David, D. S., D. Saravanan, and A. Jayachandran. 2020. Deep convolutional neural network based early diagnosis of multi class brain tumour classification system. Solid State Technol. 63:3599–623.
   Google Scholar
• Deb, D., and S. Roy. 2021. Brain tumor detection based on hybrid deep neural network in MRI by adaptive squirrel search optimization. Multimedia Tools Appl. 80:2621–45. doi: 10.1007/s11042-020-09810-9.
   Web of Science ®Google Scholar
• Gomathi, P., S. Baskar, P. M. Shakeel, and V. S. Dhulipala. 2020. Identifying brain abnormalities from electroencephalogram using evolutionary gravitational neocognitron neural network. Multimedia Tools Appl. 79:10609–28. doi:10.1007/s11042-019-7301-5.
   Web of Science ®Google Scholar
• Guan, X., G. Yang, J. Ye, W. Yang, X. Xu, W. Jiang, and X. Lai. 2022. 3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework. BMC Med. Imaging 22:1–8. https://ieee-dataport.org/competitions/brats-miccai-brain-tumor-dataset.
   PubMed Web of Science ®Google Scholar
• Huang, H., G. Yang, W. Zhang, X. Xu, W. Yang, W. Jiang, and X. Lai. 2021. A deep multi-task learning framework for brain tumor segmentation. Front Oncol. 11:690244. doi:10.3389/fonc.2021.690244.
   PubMed Web of Science ®Google Scholar
• Jangra, M., S. K. Dhull, and K. K. Singh. 2020. ECG arrhythmia classification using modified visual geometry group network (mVggnet). J. Intell. Fuzzy Syst. 38:3151–65. doi:10.3233/JIFS-191135.
   Web of Science ®Google Scholar
• Jin, Y., G. Yang, Y. Fang, R. Li, X. Xu, Y. Liu, and X. Lai. 2021. 3D PBV-Net: an automated prostate MRI data segmentation method. Comput. Biol. Med. 128:104160. doi:10.1016/j.compbiomed.2020.104160.
   PubMed Web of Science ®Google Scholar
• Kumar, S., and D. P. Mankame. 2020. Optimization driven deep convolution neural network for brain tumor classification. Biocybern. Biomed. Eng. 40:1190–204. doi:10.1016/j.bbe.2020.05.009.
   Web of Science ®Google Scholar
• Kurmi, Y., and V. Chaurasia. 2020. Classification of magnetic resonance images for brain tumour detection. Iet Image Process. 14:2808–18. doi:10.1049/iet-ipr.2019.1631.
   Web of Science ®Google Scholar
• Lavanya, S., S. V. Annlin Jeba, P. Bhuvaneswari, and F. H. Shajin. 2023. Support vector machine classifier optimized with seagull optimization algorithm for brain tumor classification. Concurr. Comput. Pract. Exp. 35:e7396. doi:10.1002/cpe.7396.
   Web of Science ®Google Scholar
• Li, H., Y. Nan, and G. Yang 2022. LKAU-Net: 3D large-kernel attention-based u-net for automatic MRI brain tumor segmentation. In Medical Image Understanding and Analysis: 26th Annual Conference, MIUA 2022, Cambridge, UK, July 27–29, 2022, 313–27. Cham: Springer International Publishing.
   Google Scholar
• Liu, Y., G. Yang, M. Hosseiny, A. Azadikhah, S. A. Mirak, Q. Miao, S. S. Raman, and K. Sung. 2020. Exploring uncertainty measures in Bayesian deep attentive neural networks for prostate zonal segmentation. IEEE. Access 8:151817–28. doi:10.1109/ACCESS.2020.3017168.
   PubMed Web of Science ®Google Scholar
• Maharjan, S., A. Alsadoon, P. W. Prasad, T. Al-Dalain, and O. H. Alsadoon. 2020. A novel enhanced softmax loss function for brain tumour detection using deep learning. J. Neurosci. Methods 330:108520. doi:10.1016/j.jneumeth.2019.108520.
   PubMed Web of Science ®Google Scholar
• Mary Adline Priya, M. 2023. Dropout AlexNet‐extreme learning optimized with fast gradient descent optimization algorithm for brain tumor classification. Concurr. Comput. Pract. Exp. 35:1–. doi:10.1002/cpe.7543.
   Web of Science ®Google Scholar
• Mehrotra, R., and M. A. Ansari. 2020. Feature extraction for brain tumour analysis and classification: A review. Int. J. Digit. Signals Smart Syst. 4:199–218. doi:10.1504/IJDSSS.2020.106083.
   Google Scholar
• Mohan, R., K. Ganapathy, and A. Rama. 2021. Brain tumour classification of magnetic resonance images using a novel CNN based medical image analysis and detection network in comparison with VGG16. J. Popul. Ther. Clin. Pharmacol. 28:e113–e125.
   Web of Science ®Google Scholar
• Nan, Y., J. Del Ser, S. Walsh, C. Schönlieb, M. Roberts, I. Selby, K. Howard, J. Owen, J. Neville, J. Guiot, et al. 2022. Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions. Information Fusion.
   Google Scholar
• Narmatha, C., S. M. Eljack, A. A. Tuka, S. Manimurugan, and M. Mustafa. 2020. A hybrid fuzzy brain-storm optimization algorithm for the classification of brain tumor MRI images. J. Ambient. Intell. Humaniz. Comput. 1–9. doi:10.1007/s12652-020-02470-5.
   PubMedGoogle Scholar
• Novak, J., N. Zarinabad, H. Rose, T. Arvanitis, L. MacPherson, B. Pinkey, A. Oates, P. Hales, R. Grundy, D. Auer, et al. 2021. Classification of paediatric brain tumours by diffusion weighted imaging and machine learning. Sci. Rep. 11:1–8. doi:10.1038/s41598-021-82214-3.
   PubMed Web of Science ®Google Scholar
• Padmapriya, K., S. Divya, and P. Ezhumalai. 2021. A scrutiny on brain tumour classification using deep learning technique. In Materials Today: Proceedings.
   Google Scholar
• Perez, E., and D. Capper. 2020. Invited review: DNA methylation‐based classification of paediatric brain tumours. Neuropathol. Appl. Neurobiol. 46:28–47. doi:10.1111/nan.12598.
   PubMed Web of Science ®Google Scholar
• Polat, Ö., and C. Güngen. 2021. Classification of brain tumors from MR images using deep transfer learning. J. Supercomput. 77:7236–52. doi:10.1007/s11227-020-03572-9.
   Web of Science ®Google Scholar
• Priesterbach‐Ackley, L. P., H. B. Boldt, J. K. Petersen, N. Bervoets, D. Scheie, B. P. Ulhøi, M. Gardberg, T. Brännström, S. H. Torp, E. Aronica, et al. 2020. Brain tumour diagnostics using a DNA methylation‐based classifier as a diagnostic support tool. Neuropathol. Appl. Neurobiol. 46:478–92. doi:10.1111/nan.12610.
   PubMed Web of Science ®Google Scholar
• Rajesh, P., S. Muthubalaji, S. Srinivasan, and F. H. Shajin. 2022. Leveraging a dynamic differential annealed optimization and recalling enhanced recurrent neural network for maximum power point tracking in wind energy conversion system. Technol. Econ. Smart Grids Sustain. Energy 7:19. doi:10.1007/s40866-022-00144-z.
   Google Scholar
• Rajesh, P., F. H. Shajin, and N. Vijaya Anand. 2021. An efficient estimation model for induction motor using BMO-RBFNN technique. Process Integr. Optim. Sustain. 5:777–92. doi:10.1007/s41660-021-00177-4.
   Google Scholar
• Rammurthy, D., and P. K. Mahesh. 2022. Whale Harris hawks optimization based deep learning classifier for brain tumor detection using MRI images. J. King Saud Univ. - Comput. Inf. Sci. 34 :3259–72. doi:10.1016/j.jksuci.2020.08.006.
   Web of Science ®Google Scholar
• Raschke, F., T. R. Barrick, T. L. Jones, G. Yang, X. Ye, and F. A. Howe. 2019. Tissue-type mapping of gliomas. NeuroImage: Clin. 21:101648. doi:10.1016/j.nicl.2018.101648.
   PubMed Web of Science ®Google Scholar
• Rehman, A., M. A. Khan, T. Saba, Z. Mehmood, U. Tariq, and N. Ayesha. 2021. Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture. Microsc. Res. Tech. 84:133–49. doi:10.1002/jemt.23597.
   PubMed Web of Science ®Google Scholar
• Rehman, A., S. Naz, M. I. Razzak, F. Akram, and M. Imran. 2020. A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits Syst. Signal Process 39:757–75. doi:10.1007/s00034-019-01246-3.
   Web of Science ®Google Scholar
• Shajin, F. H., and P. Rajesh. 2022. FPGA realization of a reversible data hiding scheme for 5G MIMO-OFDM system by chaotic key generation-based paillier cryptography along with LDPC and its side channel estimation using machine learning technique. J. Circuits Syst. Comput. 31:2250093. doi:10.1142/S0218126622500931.
   Web of Science ®Google Scholar
• Shajin, F. H., P. Rajesh, and V. K. Nagoji Rao. 2022. Efficient framework for brain tumour classification using hierarchical deep learning neural network classifier. Comput. Methods Biomech. Biomed. Eng. Imag. Vis . 1–8. doi:10.1080/21681163.2022.2111719.
   Web of Science ®Google Scholar
• Shajin, F. H., P. Rajesh, and S. Thilaha. 2020. Bald eagle search optimization algorithm for cluster head selection with prolong lifetime in wireless sensor network. J. Soft Comput. Civ. Eng. 1:7.
   Google Scholar
• Shanmuga Priya, S., S. Saran Raj, B. Surendiran, and N. Arulmurugaselvi. 2020. Brain tumour detection in MRI using deep learning. In Evolution in computational intelligence: Frontiers in intelligent computing: Theory and applications (FICTA 2020), ed. V. Bhateja, S. L. Peng, S. C. Satapathy, and Y. D. Zhang, Vol. 1, 395–403. Singapore: Springer.
   Google Scholar
• Sharath Chander, P., J. Soundarya, and R. Priyadharsini 2020. Brain tumour detection and classification using K-means clustering and SVM classifier. In RITA 2018: Proceedings of the 6th International Conference on Robot Intelligence Technology and Applications, Singapore, 49–63. Springer.
   Google Scholar
• Soltaninejad, M., G. Yang, T. Lambrou, N. Allinson, T. L. Jones, T. R. Barrick, F. A. Howe, and X. Ye. 2018. Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Comput. Methods Programs Biomed. 157:69–84. doi:10.1016/j.cmpb.2018.01.003.
   PubMed Web of Science ®Google Scholar
• Sultan, H. H., N. M. Salem, and W. Al-Atabany. 2019. Multi-classification of brain tumor images using deep neural network. IEEE. Access 7:69215–25. doi:10.1109/ACCESS.2019.2919122.
   Google Scholar
• Tandel, G. S., A. Balestrieri, T. Jujaray, N. N. Khanna, L. Saba, and J. S. Suri. 2020. Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm. Comput. Biol. Med. 122:103804. doi:10.1016/j.compbiomed.2020.103804.
   PubMed Web of Science ®Google Scholar
• Uthra Devi, K., and R. Gomathi. 2021. Brain tumor classification using saliency driven nonlinear diffusion and deep learning with convolutional neural networks (CNN). J. Ambient. Intell. Humaniz. Comput. 12:6263–73. doi:10.1007/s12652-020-02200-x.
   Web of Science ®Google Scholar
• Wang, S. H., S. L. Fernandes, Z. Zhu, and Y. D. Zhang. 2021. AVNC: Attention-based VGG-style network for COVID-19 diagnosis by CBAM. IEEE Sens. J. 22:17431–38. doi:10.1109/JSEN.2021.3062442.
   PubMedGoogle Scholar
• Wang, S. H., M. A. Khan, and Y. D. Zhang. 2022. VISPNN: VGG-inspired stochastic pooling neural network. Comput. Mater. Contin. 70:3081. doi:10.32604/cmc.2022.019447.
   PubMed Web of Science ®Google Scholar
• Yang, H., Y. Cheng, and G. Li. 2021. A denoising method for ship radiated noise based on spearman variational mode decomposition, spatial-dependence recurrence sample entropy, improved wavelet threshold denoising, and Savitzky-Golay filter. Alex. Eng. J. 60:3379–400. doi:10.1016/j.aej.2021.01.055.
   Web of Science ®Google Scholar
• Yang, G., Q. Ye, and J. Xia. 2022. Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. Inform. Fusion 77:29–52. doi:10.1016/j.inffus.2021.07.016.
   PubMed Web of Science ®Google Scholar
• Ye, Q., J. Xia, and G. Yang 2021. Explainable AI for COVID-19 CT classifiers: An initial comparison study. In 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), Aveiro, Portugal, 521–26. IEEE.
   Google Scholar
• Yin, B., C. Wang, and F. Abza. 2020. New brain tumor classification method based on an improved version of whale optimization algorithm. Biomed. Signal Process. Control 56:101728. doi:10.1016/j.bspc.2019.101728.
   Web of Science ®Google Scholar
• Zhang, W., G. Yang, H. Huang, W. Yang, X. Xu, Y. Liu, and X. Lai. 2021. ME‐net: Multi‐encoder net framework for brain tumor segmentation. Int. J. Imag. Syst. Technol. 31:1834–48. doi:10.1002/ima.22571.
   Web of Science ®Google Scholar
• Zhang, Y. D., G. Zhao, J. Sun, X. Wu, Z. H. Wang, H. M. Liu, V. V. Govindaraj, T. Zhan, and J. Li. 2018. Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and Jaya algorithm. Multimedia Tools Appl. 77:22629–22648. doi:10.1007/s11042-017-5023-0.
   Web of Science ®Google Scholar