Hybrid deep learning algorithm for brain tumour detection

References

• Nawaz M, Nazir T, Masood M, et al. Analysis of brain MRI images using improved cornernet approach. Diagnostics. 2021;11(10):1856.
   Web of Science ®Google Scholar
• Manic KS, Biju R, Patel W, et al. Extraction and evaluation of corpus callosum from 2D brain MRI slice: a study with cuckoo search algorithm. Comput Math Methods Med. 2021;2021:Article ID 5524637. https://doi.org/10.1155/2021/5524637.
   Web of Science ®Google Scholar
• Aziz A, Attique M, Tariq U, et al.. An ensemble of optimal deep learning features for brain tumor classification. Computers Materials & Continua. 2021;69(2):2653.
   Web of Science ®Google Scholar
• Aboelenein NM, Songhao P, Koubaa A, et al. HTTU-Net: hybrid two track U-net for automatic brain tumor segmentation. IEEE Access. 2020;8:101406–101415.
   Google Scholar
• Goyal M, Bhardwaj VK, Prakash A. Investigating new positive, bounded, and convergent numerical solution for the nonlinear time-dependent breast cancer dynamic competition model. Math Methods Appl Sci. 2021;44(6):4636–4653.
   Web of Science ®Google Scholar
• Ferdous GJ, Sathi KA, Hossain MA. Application of hybrid classifier for multi-class classification of MRI brain tumor images. In: 2021 5th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), Dhaka, Bangladesh; 2021. p. 1–6. https://doi.org/10.1109/ICEEICT53905.2021.9667829.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Huang Z, Tang J, Shan G, et al. An efficient passenger-hunting recommendation framework with multitask deep learning. IEEE Int Things J. 2019;6(5):7713–7721.
   Web of Science ®Google Scholar
• Tahir ABT, Khan MA, Alhaisoni M, et al.. Deep learning and improved particle swarm optimization based multimodal brain tumor classification. Computers Materials & Continua. 2021;68(1):1099.
   Web of Science ®Google Scholar
• Rehman A, Khan MA, Saba T, et al. Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture. Microsc Res Tech. 2021;84(1):133–149.
   PubMed Web of Science ®Google Scholar
• Xiong H, Liu J, Zhang R, et al. An accurate vehicle and road condition estimation algorithm for vehicle networking applications. IEEE Access. 2019;7:17705–17715.
   Web of Science ®Google Scholar
• Verma R, Shrivastava A, De K, et al. A smartphone-based passenger assistant for public bus commute in developing countries. IEEE Trans Comput Soc Syst. 2020;7(2):465–476.
   Web of Science ®Google Scholar
• Xiao Z, Xu S, Li T, et al. On extracting regular travel behavior of private cars based on trajectory data analysis. IEEE Trans Veh Technol. 2020;69(12):14537–14549.
   Web of Science ®Google Scholar
• Singh LK, Garg H, Khanna M, et al. An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus. Med Biol Eng Comput. 2021;59(2):333–353.
   PubMed Web of Science ®Google Scholar
• Gupta N, Garg H, Agarwal R. A robust framework for glaucoma detection using CLAHE and EfficientNet. Vis Comput. 2022;38(7):2315–2328.
   Web of Science ®Google Scholar
• Derikvand F, Khotanlou H. Brain tumor segmentation in MRI images using a hybrid deep network based on patch and pixel. In: 2020 International Conference on Machine Vision and Image Processing (MVIP), Iran; 2020. p. 1–5. https://doi.org/10.1109/MVIP49855.2020.9116880.
   Google Scholar
• Fang F, Yao Y, Zhou T, et al. Self-supervised multi-modal hybrid fusion network for brain tumor segmentation. IEEE J Biomed Health Inform; 2021;26(11):5310–5320. https://doi.org/10.1109/JBHI.2021.3109301.
   Web of Science ®Google Scholar
• Majib MS, Rahman MM, Sazzad TS, et al. Vgg-scnet: a vgg net-based deep learning framework for brain tumor detection on mri images. IEEE Access. 2021;9:116942–116952.
   Google Scholar
• Sravan V, Swaraja K, Meenakshi K, et al. Magnetic resonance images based brain tumor segmentation-a critical survey. In: 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI) (48184), Tirunelveli, India; 2020. p. 1063–1068. https://doi.org/10.1109/ICOEI48184.2020.9143045.
   Google Scholar
• Yadav N, Jain PK. Brain tumor detection and optimization using hybrid classification algorithm. In: 2021 Fifth International Conference on I-Smac (Iot in Social, Mobile, Analytics and Cloud)(I-Smac), Palladam, India; 2021. p. 1148–1153. https://doi.org/10.1109/I-SMAC52330.2021.9641033.
   Google Scholar
• Rasyid DA, Huang GH, Iriawan N. Segmentation of low-grade gliomas using U-net VGG16 with transfer learning. In: 2021 11th International Conference on Cloud Computing, Data Science & Engineering (confluence), Noida, India; 2021. p. 393–398. https://doi.org/10.1109/Confluence51648.2021.9377093.
   Google Scholar
• Takács P, Manno-Kovacs A. Mri brain tumor segmentation combining saliency and convolutional network features. In: 2018 International Conference on Content-Based Multimedia Indexing (CBMI), La Rochelle, France; 2018. p. 1–6. https://doi.org/10.1109/CBMI.2018.8516544.
   Google Scholar
• Biswas A, Islam MS. Hybrid CNN-SVM model for brain tumor classification utilizing different datasets. In: 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), Khulna, Bangladesh; 2021. p. 1–4. https://doi.org/10.1109/ICECIT54077.2021.9641201.
   Google Scholar
• Duan Y, Liu F, Jiao L, et al. SAR image segmentation based on convolutional-wavelet neural network and Markov random field. Pattern Recognit. 2017;64:255–267.
   Web of Science ®Google Scholar
• Sammouda R, Aboalsamh H, Saeed F. Comparison between K mean and fuzzy C-mean methods for segmentation of near infrared fluorescent image for diagnosing prostate cancer. In: International Conference on Computer Vision and Image Analysis Applications, Sousse, Tunisia; 2015. p. 1–6. https://doi.org/10.1109/ICCVIA.2015.7351905.
   Google Scholar
• Naik PPS, Gopal TV. A novel approach for color image segmentation using iterative partitioning mean shift clustering algorithm. In: 2015 International Conference on Communications and Signal Processing (ICCSP), Melmaruvathur, India; 2015. p. 1516–1519. https://doi.org/10.1109/ICCSP.2015.7322768.
   Google Scholar
• Toğaçar M, Ergen B, Cömert Z. Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks. Med Biol Eng Comput. 2021;59(1):57–70.
   PubMed Web of Science ®Google Scholar
• Khan MA, Arshad H, Nisar W, et al. An Integrated Design of Fuzzy C-Means and NCA-Based Multi-properties Feature Reduction for Brain Tumor Recognition. In: E Priya, V Rajinikanth, editors. Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems. Singapore: Springer; 2021. https://doi.org/10.1007/978-981-15-6141-2_1.
   Google Scholar
• Khan MA, Ashraf I, Alhaisoni M, et al. Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists. Diagnostics. 2020;10(8):565.
   Web of Science ®Google Scholar
• Mridha K, Pandey AP, Ranpariya A, et al. Web based brain tumor detection using neural network. In: 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), Arad, Romania; 2021. p. 137–143. https://doi.org/10.1109/ICCCA52192.2021.9666248.
   Google Scholar
• Ramesh K, Kumar GK, Swapna K, et al. A review of medical image segmentation algorithms. EAI Endorsed Trans Pervasive Health Technol. 2021;7(27):e6–e6.
   Google Scholar
• Deb D, Roy S. Brain tumor detection based on hybrid deep neural network in MRI by adaptive squirrel search optimization. Multimed Tools Appl. 2021;80(2):2621–2645.
   Web of Science ®Google Scholar
• Sasank V, Venkateswarlu S. Hybrid deep neural network with adaptive rain optimizer algorithm for multi-grade brain tumor classification of MRI images. Multimed Tools Appl. 2022;81(6):8021–8057.
   Web of Science ®Google Scholar
• Jalali V, Kaur D. A study of classification and feature extraction techniques for brain tumor detection. Int J Multimed Inf Retr. 2020;9(4):271–290.
   Web of Science ®Google Scholar
• Chanu MM, Thongam K. Computer-aided detection of brain tumor from magnetic resonance images using deep learning network. J Ambient Intell Humaniz Comput. 2021;12(7):6911–6922.
   Web of Science ®Google Scholar
• Jena B, Nayak GK, Saxena S. An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature. Mach Vis Appl. 2022;33(1):1–16.
   Web of Science ®Google Scholar
• Bhatele KR, Bhadauria SS. Brain structural disorders detection and classification approaches: a review. Artif Intell Rev. 2020;53(5):3349–3401.
   Web of Science ®Google Scholar
• Uthra Devi K, Gomathi R. Brain tumour classification using saliency driven nonlinear diffusion and deep learning with convolutional neural networks (CNN). J Ambient Intell Humaniz Comput. 2021;12(6):6263–6273.
   Web of Science ®Google Scholar
• Chouhan SS, Kaul A, Singh UP. Image segmentation using computational intelligence techniques. Arch Comput Methods Eng. 2019;26(3):533–596.
   Web of Science ®Google Scholar
• Al-Galal SAY, Alshaikhli IFT, Abdulrazzaq M. MRI brain tumor medical images analysis using deep learning techniques: a systematic review. Health Technol (Berl). 2021;11(2):267–282.
   Web of Science ®Google Scholar