References
- Louis DN, Perry A, Reifenberger G, et al. The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 2016;131(6):803–820.
- Alanazi MF, Ali MU, Hussain SJ, et al. Brain tumor/mass classification framework using magnetic-resonance-imaging-based isolated and developed transfer deep-learning model. Sensors. 2022;22(1):372.
- Swati ZNK, Zhao Q, Kabir M, et al. Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph. 2019;75:34–46.
- Zahid U, Ashraf I, Khan MA, et al. Brainnet: optimal deep learning feature fusion for brain tumor classification. Comput Intell Neurosci. 2022;2022.
- Liu Z, Tong L, Chen L, et al. Deep learning based brain tumor segmentation: a survey. Complex Intell Syst. 2022;9:1001–1026.
- Ghaffari M, Sowmya A, Oliver R. Automated brain tumour segmentation using cascaded 3d densely-connected u-net. In: International MICCAI Brainlesion Workshop. Cham: Springer; 2021. p. 481–491.
- Nayak DR, Padhy N, Mallick PK, et al. Brain tumor classification using dense efficient-net. Axioms. 2022;11(1):34.
- Raza R, Bajwa UI, Mehmood Y, et al. dResU-Net: 3D deep residual U-Net based brain tumor segmentation from multimodal MRI. Biomed Signal Process Control. 2023;79:103861.
- Bal A, Banerjee M, Chakrabarti A, et al. MRI brain tumor segmentation and analysis using rough-fuzzy c-means and shape based properties. J King Saudi Univ-Comput Inf Sci. 2022;34(2):115–133.
- Yeruva AR, Durga CSLV, Gokulavasan B, et al. A smart healthcare monitoring system based on fog computing architecture. In: Proceeding of 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS). Tashkent, Uzbekistan: IEEE; 2022.
- Ottom MA, Rahman HA, Dinov ID. Znet: deep learning approach for 2D MRI brain tumor segmentation. IEEE J Transl Eng Health Med. 2022.
- Xie Y, Zaccagna F, Rundo L, et al. Convolutional neural network techniques for brain tumor classification (from 2015 to 2022): review, challenges, and future perspectives. Diagnostics. 2022;12(8):1850.
- Sharif MI, Khan MA, Alhussein M, et al. A decision support system for multimodal brain tumor classification using deep learning. Complex Intell Syst. 2022;8(4):3007–3020.
- Kesav N, Jibukumar MG. Efficient and low complex architecture for detection and classification of brain tumor using RCNN with two channel CNN. J King Saud Univ-Comput Inf Sci. 2022;34(8):6229–6242.
- Hassan I, Umar M, Dokoro AH. An innovative prototype for diagnosing and treatment of breast cancer: A case study of specialist hospital gombe. Multimed Res. 2022;5(2).
- Gokulkumari G. Classification of brain tumor using manta Ray foraging optimization-based DeepCNN classifier. Multimed Res. 2020;3(4):32–42.
- Ghassemi N, Shoeibi A, Rouhani M. Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomed Signal Process Control. 2020;57:101678.
- Zahoor MM, Qureshi SA, Bibi S, et al. A new deep hybrid boosted and ensemble learning-based brain tumor analysis using MRI. Sensors. 2022;22(7):2726.
- Magadza T, Viriri S. Deep learning for brain tumor segmentation: a survey of state-of-the-art. J Imaging. 2021;7(2):19.
- Raja PS. Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach. Biocybern Biomed Eng. 2020;40(1):440–453.
- Mzoughi H, Njeh I, Wali A, et al. Deep multi-scale 3D convolutional neural network (CNN) for MRI gliomas brain tumor classification. J Digit Imaging. 2020;33(4):903–915.
- Raza A, Ayub H, Khan JA, et al. A hybrid deep learning-based approach for brain tumor classification. Electronics (Basel). 2022;11(7):1146.
- Younis A, Qiang L, Nyatega CO, et al. Brain tumor analysis using deep learning and VGG-16 ensembling learning approaches. Appl Sci. 2022;12(14):7282.
- Jayasudha S. Comparision of preprocess techniques for brain image using machine learning. Information Technology in Industry. 2021;9(3):653–656.
- Zhang W, Pang J, Chen K, et al. K-net: towards unified image segmentation. Adv Neural Inf Process Syst. 2021;34:10326–10338.
- Renjit A. DeepJoint segmentation for the classification of severity-levels of glioma tumour using multimodal MRI images. IET Image Proc. 2020;14(11):2541–2552.
- Dehghani M, Trojovská E, Trojovský P. Driving training-based optimization: a new human-based metaheuristic algorithm for solving optimization problems. 2022.
- Alamelu Mangai S, Ravi Sankar B, Alagarsamy K. Taylor series prediction of time series data with error propagated by artificial neural network. Int J Comput Appl. 2014;89(1).
- Kannan P, ShanthaSelva Kumari R. VLSI architecture for LGXP texture for face recognition. J Intell Fuzzy Syst. 2014;27(5):2635–2647.
- Liu C, Wechsler H. A Gabor feature classifier for face recognition. In: Vol. 2, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV; 2001. IEEE; July 2001. p. 270–275.
- Ahonen T, Matas J, He C, et al. Rotation invariant image description with local binary pattern histogram fourier features. In: Proceedings of Scandinavian Conference on Image Analysis. Berlin, Heidelberg: Springer; June 2009.p. 61–70.
- Larsen ABL, Vestergaard JS, Larsen R. HEp-2 cell classification using shape index histograms with donut-shaped spatial pooling. IEEE Trans Med Imaging. 2014;33(7):1573–1580.
- Zhang W, Shan S, Gao W, et al. Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition. In: Vol. 1, Proceedings of Tenth IEEE International Conference on Computer Vision (ICCV'05). IEEE; October 2005.p. 786–791.
- Ren JS, Xu L, Yan Q, et al. Shepard convolutional neural networks. Adv Neural Inf Process Syst. 2015;28.
- Kim B, Kehtarnavaz N, LeBoulluec P, et al. Automation of ROI extraction in hyperspectral breast images. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; July 2013. p. 3658–3661.
- Cha SH. Comprehensive survey on distance/similarity measures between probability density functions. City. 2007;1(2):1.
- Iqbal N, Mumtaz R, Shafi U, et al. Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms. PeerJ Comput Sci. 2021;7:e536.
- Bahadure NB, Ray AK, Thethi HP. Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int J Biomed Imaging. 2017;1:1–12.
- BRATS. 2018 dataset is taken from “https://www.med.upenn.edu/sbia/brats2018/data.html”, accessed on September 2022.
- Sattar D, Salim R. A smart metaheuristic algorithm for solving engineering problems. Eng Comput. 2021;37(3):2389–2417.
- Pan JS, Zhang LG, Wang RB, et al. Gannet optimization algorithm: a new metaheuristic algorithm for solving engineering optimization problems. Math Comput Simul. 2022;202:343–373.
- Naruei I, Keynia F. A new optimization method based on COOT bird natural life model. Expert Syst Appl. 2021;183:115352.
- Badrinarayanan V, Kendall A, Cipolla R. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39(12):2481–2495.
- Çiçek Ö, Abdulkadir A, Lienkamp SS, et al. (2016). 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer; October 2016. p. 424–432.