Brain tumour classification using MRI images based on lenet with golden teacher learning optimization

References

• Aamir M, Rahman Z, Dayo ZA, Abro WA, Uddin MI, Khan I, Imran AS, Ali Z, Ishfaq M, Guan Y, et al. 2022. A deep learning approach for brain tumor classification using MRI images. Comput Electr Eng101. 101:108105. doi: 10.1016/j.compeleceng.2022.108105.
   Web of Science ®Google Scholar
• Alqazzaz S, Sun X, Yang X, Nokes L. 2019. Automated brain tumor segmentation on multi-modal MR image using SegNet. Comput Vis Media. 5(2):209–219. doi: 10.1007/s41095-019-0139-y.
   Google Scholar
• Amin J, Sharif M, Yasmin M, Fernandes SL. 2018. Big data analysis for brain tumor detection: deep convolutional neural networks. Future Gener Comput Syst 87. 87:290–297. doi: 10.1016/j.future.2018.04.065.
   Web of Science ®Google Scholar
• Amou MA, Xia K, Kamhi S, Mouhafid M. 2022. A novel MRI diagnosis method for brain tumor classification based on CNN and bayesian optimization. Healthcare. 10(3):494. doi: 10.3390/healthcare10030494.
   Web of Science ®Google Scholar
• Arif M, Ajesh F, Shamsudheen S, Geman O, Izdrui D, Vicoveanu D. 2022. Brain tumor detection and classification by MRI. using biologically inspired orthogonal wavelet transform and deep learning techniques. J Healthc Eng. doi: 10.1155/2022/2693621.
   Web of Science ®Google Scholar
• Bai Y, Guo L, Jin L, Huang Q, 2009. A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition. In proceedings of 2009 16th IEEE International Conference on Image Processing (ICIP), Cairo. p. 3305–3308.
   Google Scholar
• BRATS. 2018. databasetaken from https://www.med.upenn.edu/sbia/brats2018data.html accessed on September 2022.
   Google Scholar
• Chen J, Yang G, Khan H, Zhang H, Zhang Y, Zhao S, Mohiaddin R, Wong T, Firmin D, Keegan J. 2021. JAS-GAN: generative adversarial network based joint atrium and scar segmentations on unbalanced atrial targets. IEEE J Biomed Health Inform. 26(1):103–114. doi: 10.1109/JBHI.2021.3077469.
   Web of Science ®Google Scholar
• Chilakala LR, Kishore GN. 2021. Optimal deep belief network with opposition‐based hybrid grasshopper and honeybee optimization algorithm for lung cancer classification: a DBNGHHB approach. Int J Imaging Syst Technol. 31(3):1404–1423. doi: 10.1002/ima.22515.
   Web of Science ®Google Scholar
• Deb D, Roy S. 2021. Brain tumor detection based on hybrid deep neural network in MRI. By adaptive squirrel search optimization. Multimedia Tools Appl. 80(2):2621–2645. doi: 10.1007/s11042-020-09810-9.
   Web of Science ®Google Scholar
• Fausto F, Cuevas E, Gonzales A. 2017. A new descriptor for image matching based on bionic principles. Pattern Anal Appl. 20(4):1245–1259. doi: 10.1007/s10044-017-0605-z.
   Web of Science ®Google Scholar
• Figshare. 2022. dataset taken from accessed on September 2022. https://figshare.com/articles/brain_tumor_dataset/1512427.
   Google Scholar
• Guan X, Yang G, Ye J, Yang W, Xu X, Jiang W, Lai X. 20223. AGSE-VNet: an automatic brain tumor MRI data segmentation framework. United Kingdom: BMC Medical Imaging.22.
   Google Scholar
• Gunasekara SR, Kaldera HNTK, Dissanayake MB. 2021. A systematic approach for MRI. brain tumor localization and segmentation using deep learning and active contouring. J Healthc Eng. 2021.
   PubMed Web of Science ®Google Scholar
• Hasan MK, Ahamad MA, Yap CH, Yang G. 2023. A survey, review, and future trends of skin lesion segmentation and classification. Comput Biol Med. 155. doi: 10.1016/j.compbiomed.2023.106624.
   Web of Science ®Google Scholar
• Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H. 2017. Brain tumor segmentation with deep neural networks. Med Image Anal. 35(35):18–31.
   PubMedGoogle Scholar
• Huang H, Yang G, Zhang W, Xu X, Yang W, Jiang W, Lai X. 2021. A deep multi-task learning framework for brain tumor segmentation. Front Oncol. 11:11. doi: 10.3389/fonc.2021.690244.
   Web of Science ®Google Scholar
• Hung TY, Fan KC2014. Local vector pattern in high-order derivative space for face recognition. In proceedings of 2014 IEEE International Conference on Image Processing (ICIP)), Paris, France. p. 239–243.
   Google Scholar
• Jin Y, Yang G, Fang Y, Li R, Xu X, Liu Y, Lai X. 2021 3. PBV-Net: An automated prostate MRI data segmentation method. IEEE J Biomed Health Informat. 128.
   Google Scholar
• Kalaiselvi T, Padmapriya T, Sriramakrishnan P, Priyadharshini V. 2020. Development of automatic glioma brain tumor detection system using deep convolutional neural networks. Int J Imaging Syst Technol. 30(4):926–938.
   Web of Science ®Google Scholar
• Khairandish MO, Sharma M Jain V Chatterjee JM, and Jhanjhi NZ. 2021. A hybrid CNN-SVM threshold segmentation approach for tumor detection and classification of MRI. brain images. IRBM. 43(4):290–299.
   Web of Science ®Google Scholar
• LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature. 521(7553):436–444. doi: 10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• Li H, Nan Y, Ser JD, Yang G. 2022. Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation. Neural Comput Appl. 35(30):1–15. doi: 10.1007/s00521-022-08016-4.
   PubMed Web of Science ®Google Scholar
• Li H, Nan Y, Ser JD, Yang G. 2023. Large-Kernel Attention for 3D Medical Image Segmentation. Cognit Comput. 1–15. doi: 10.1007/s12559-023-10126-7.
   PubMed Web of Science ®Google Scholar
• Li H, Nan Y, Yang G 2022.LKAU-Net: 3D Large-Kernel Attention-Based U-Net for Automatic MRI Brain Tumor Segmentation. In Annual Conference on Medical Image Understanding and Analysis. 313–327, Springer, Cham.
   Google Scholar
• Liu Y, Yang G, Hosseiny M, Azadikhah A, Mirak SA, Miao Q, Raman SS, Sung K. 2020. Exploring uncertainty measures in bayesian deep attentive neural networks for prostate zonal segmentation. IEEE Acces. 8:151817–151828. doi: 10.1109/ACCESS.2020.3017168.
   PubMed Web of Science ®Google Scholar
• Lu S, Zhang Z, Yan Z, Wang Y, Cheng T, Zhou R, Yang G. 2023a. Mutually aided uncertainty incorporated dual consistency regularization with pseudo label for semi-supervised medical image segmentation. Neurocomputing. 548. doi: 10.1016/j.neucom.2023.126411.
   Web of Science ®Google Scholar
• Lu S, Zhang Z, Yan Z, Wang Y, Cheng T, Zhou R, Yang G. 2023b. Mutually aided uncertainty incorporated dual consistency regularization with pseudo label for semi-supervised medical image segmentation.Neurocomputing. Neurocomputing. 548:548. doi: 10.1016/j.neucom.2023.126411.
   Web of Science ®Google Scholar
• Majib MS, Rahman MM, Sazzad TS, Khan NI, Dey SK. 2021. Vgg-scnet: a vgg net-based deep learning framework for brain tumor detection on mri images. IEEE Acces. 9:116942–116952. doi: 10.1109/ACCESS.2021.3105874.
   Google Scholar
• Masood M, Nazir T, Nawaz M, Mehmood A, Rashid J, Kwon HY, Mahmood T, Hussain A. 2021. A novel deep learning method for recognition and classification of brain tumors from MRI. images. Diagnostics. 11(5):744. doi: 10.3390/diagnostics11050744.
   Web of Science ®Google Scholar
• Nan Y, Ser JD, Tang Z, Tang P, Xing X, Fang Y, Herrera F, Pedrycz W, Walsh S, Yang G. 2023. Fuzzy Attention neural network to tackle discontinuity in airway segmentation. IEEE Trans Neural Net Learn Sys. 1–14. doi: 10.1109/TNNLS.2023.3269223.
   Google Scholar
• Nan Y, Ser JD, Walsh S, Schönlieb C, Roberts M, Selby I, Howard K, Owen J, Neville J, Guiot J, et al. 2022. Data harmonisation for information fusion in digital healthcare: a state-of-the-art systematic review, meta-analysis and future research directions. Inform Fusion. 82:99–122. doi: 10.1016/j.inffus.2022.01.001.
   PubMed Web of Science ®Google Scholar
• Noreen N, Palaniappan S, Qayyum A, Ahmad I, Imran M, Shoaib M. 2020. A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Acces. 8:55135–55144. doi: 10.1109/ACCESS.2020.2978629.
   Google Scholar
• Noroozi M, Mohammadi H, Efatinasab E, Lashgari A, Eslami M, Khan B. 2022. Golden search optimization algorithm. IEEE Acces. 10:37515–37532. doi:10.1109/ACCESS.2022.3162853.
   Google Scholar
• Pickup L, Nugent B, Bowie P. 2019. A preliminary ergonomic analysis of the MRI work system environment: implications and recommendations for safety and design. Radiography. 25(4):339–345. doi: 10.1016/j.radi.2019.04.001.
   PubMed Web of Science ®Google Scholar
• Rammurthy D, Mahesh PK. 2020. Whale Harris hawks optimization based deep learning classifier for brain tumor detection using MRI. images. J King Saud Univ - Comput Inf Sci. 34(6):3259–3272.
   Web of Science ®Google Scholar
• Rao RV, Savsani VJ, Vakharia DP. 2011. Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Design. 43(3):303–315. doi: 10.1016/j.cad.2010.12.015.
   Web of Science ®Google Scholar
• Raschke F, Barrick TR, Jones TL, Yang G, Ye X, Howe FA. 2019. Tissue-type mapping of gliomas. NeuroImage Clin. 21:101648. NeuroImage: Clinical. 21. doi: 10.1016/j.nicl.2018.101648.
   PubMed Web of Science ®Google Scholar
• RJS R, SJ S, Pustokhina IV, Pustokhin DA, Gupta D, Shankar KJIA. 2020. Optimal feature selection-based medical image classification using deep learning model in internet of medical things. IEEE Acces. 8:58006–58017. doi: 10.1109/ACCESS.2020.2981337.
   Google Scholar
• Rousselle F, Knaus C, Zwicker M. 2012. Adaptive rendering with non-local means filtering. ACM Trans Graphics (TOG). 31(6):1–11. doi: 10.1145/2366145.2366214.
   Web of Science ®Google Scholar
• Saba T, Mohamed AS, El-Affendi M, Amin J, Sharif M. 2020. Brain tumor detection using fusion of hand crafted and deep learning features. Cogn Syst Res. (59):221–230. doi:10.1016/j.cogsys.2019.09.007.
   Google Scholar
• Sarker S, Chowdhury S, Laha S, Dey D. 2012. Use of non-local means filter to denoise image corrupted by salt and pepper noise. Sig Img Proc. 3(2):223. doi: 10.5121/sipij.2012.3217.
   Google Scholar
• Sharif MI, Khan MA, Alhussein M, Aurangzeb K, Raza M. 2022. A decision support system for multimodal brain tumor classification using deep learning. Complex Intell Syst. 8(4):3007–3020. doi: 10.1007/s40747-021-00321-0.
   Web of Science ®Google Scholar
• Siegel RL, Miller KD, Jemal A. 2019. Cancer statistics, 2019. CA Cancer J Clin. 69(1):7–34. doi: 10.3322/caac.21551.
   PubMed Web of Science ®Google Scholar
• Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X. 2018. Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Methods Programs In Biomed. 157:69–84. doi: 10.1016/j.cmpb.2018.01.003.
   PubMed Web of Science ®Google Scholar
• Sriramakrishnan P, Kalaiselvi T, Rajeswaran R. 2019. Modified local ternary patterns technique for brain tumour segmentation and volume estimation from MRI multi-sequence scans with GPU CUDA machine. Biocybern Biomed Eng. 39(2):470–487. doi: 10.1016/j.bbe.2019.02.002.
   Web of Science ®Google Scholar
• Sultan HH, Salem NM, Al-Atabany W. 2019. Multi-classification of brain tumor images using deep neural network. IEEE Acces. 7:69215–69225. doi: 10.1109/ACCESS.2019.2919122.
   Google Scholar
• Tang Z, Yang N, Walsh S, Yang G. 2023. Adversarial transformer for repairing human airway segmentation. IEEE J Biomed Health Inform. 27(10):5015–5022. doi: 10.1109/JBHI.2023.3290136.
   PubMed Web of Science ®Google Scholar
• Wahlang I, Sharma P, Sanyal S, Saha G, Maji AK. 2020. Deep learning techniques for classification of brain MRI. Intern J Intell Syst Technol Appl. 19(6):571–588. doi: 10.1504/IJISTA.2020.112441.
   Google Scholar
• Yang G, Ye Q, Xia J. 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.Information fusion. Inform Fusion. 77:29–52. doi: 10.1016/j.inffus.2021.07.016.
   PubMed Web of Science ®Google Scholar
• Ye Q, Xia J, Yang G 2021. Explainable AI for COVID-19 CT classifiers: an initial comparison study. In the proceeding of IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), IEEE, Aveiro, Portugal.
   Google Scholar
• Zbontar J, Knoll F, Sriram A, Murrell T, Huang Z, Muckley MJ, Defazio A, Stern R, Johnson P, Bruno M, et al. 2018. fastMRI: an open dataset and benchmarks for accelerated MRI. Xiv preprint arXiv:1811.08839.
   Google Scholar
• Zhang W, Yang G, Huang H, Yang W, Xu X, Liu Y, Lai X. 2021. ME-Net: Multi-encoder net framework for brain tumor segmentation. Int J Imaging Syst Technol. 31(4):1834–1848. doi: 10.1002/ima.22571.
   Web of Science ®Google Scholar
• Zulpe N, Pawar V. 2012. GLCM textural features for brain tumor classification. Int J comput sci. 9(3):354–359.
   Google Scholar