Classification of brain tumours from MR images with an enhanced deep learning approach using densely connected convolutional network

References

• https://figshare.com/articles/brain_tumor_%26#x00A0;data%26#x00A0;set/1512427
   Google Scholar
• Adedigba AP, Adeshina SA, Aina OE, Aibinu AM. 2021. Optimal hyperparameter selection of deep learning models for COVID-19 chest X-ray classification. Intell-Based Med. 5:100034. doi:10.1016/j.ibmed.2021.100034.
   Google Scholar
• Albahli S, Ayub N, Shiraz M. 2021. Coronavirus disease (COVID-19) detection using X-ray images and enhanced DenseNet. Appl Soft Comput. 110:107645. doi:10.1016/j.asoc.2021.107645.
   Google Scholar
• Ansari MA, Mehrotra R, Agrawal R. 2020. Detection and classification of brain tumor in MRI images using wavelet transform and support vector machine. J Interdiscip Math. 23(5):955–966. doi:10.1080/09720502.2020.1723921.
   Google Scholar
• Bohmrah MK, Kaur H. 2021. Classification of Covid-19 patients using efficient fine-tuned deep learning DenseNet model. Global Transitions Proceedings. doi:10.1016/j.gltp.2021.08.003.
   Google Scholar
• Cheng J, Huang W, Cao S, Yang R, Yang W, Yun Z, Feng Q. 2015. Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS One. 10(10):e0140381. doi:10.1371/journal.pone.0144479.
   Google Scholar
• Deepak S, Ameer P. 2019. Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med. 111:103345. doi:10.1016/j.compbiomed.2019.103345.
   Google Scholar
• Gupta A, Verma A, Kaushik D, Garg M. 2020. Applying deep learning approach for brain tumor detection. Mater Today: Proc. doi:10.1016/j.matpr.2020.10.063.
   Google Scholar
• Hashemzehi R, Mahdavi SJS, Kheirabadi M, Kamel SR. 2020. Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE. Biocybern Biomed Eng. 40(3):1225–1232. doi:10.1016/j.bbe.2020.06.001.
   Google Scholar
• Huang G, Liu Z, Maaten LVD, Weinberger KQ. 2017. Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/CVPR.2017.243.
   Google Scholar
• Ismael SAA, Mohammed A, Hefny H. 2020. An enhanced deep learning approach for brain cancer MRI images classification using residual networks. Artif Intell Med. 102:101779. doi:10.1016/j.artmed.2019.101779.
   Google Scholar
• Kumar S, Mankame DP. 2020. Optimization driven deep convolution neural network for brain tumor classification. Biocybern Biomed Eng. 40(3):1190–1204. doi:10.1016/j.bbe.2020.05.009.
   Google Scholar
• Lee W-Y, Park S-M, Sim K-B. 2018. Optimal hyperparameter tuning of convolutional neural networks based on the parameter-setting-free harmony search algorithm. Optik. 172:359–367. doi:10.1016/j.ijleo.2018.07.044.
   Google Scholar
• Lu S-Y, Wang S-H, Zhang Y-D. 2020. A classification method for brain MRI via MobileNet and feedforward network with random weights. Pattern Recognit Lett. 140:252–260. doi:10.1016/j.patrec.2020.10.017.
   Google Scholar
• Maharjan S, Alsadoon A, Prasad P, Al-Dalain T, Alsadoon OH. 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.
   Google Scholar
• Mehrotra R, Ansari M, Agrawal R, Anand R. 2020. A transfer learning approach for AI-based classification of brain tumors. Mach Learn Appl. 2:100003. doi:10.1016/j.mlwa.2020.100003.
   Google Scholar
• Menze B, Isensee F, Wiest R, Wiestler B, Maier-Hein K, Reyes M, Bakas S. 2021. Analyzing magnetic resonance imaging data from glioma patients using deep learning. Comput Med Imaging Graph. 88:101828. doi:10.1016/j.compmedimag.2020.101828.
   Google Scholar
• Nazir M, Shakil S, Khurshid K. 2021. Role of deep learning in brain tumor detection and classification (2015 to 2020): a review. Comput Med Imaging Graph. 91:101940. doi:10.1016/j.compmedimag.2021.101940.
   Google Scholar
• Rammurthy D, Mahesh P. 2020. Whale Harris hawks optimization based deep learning classifier for brain tumor detection using MRI images. J King Saud Univ - Comput Inf Sci. doi:10.1016/j.jksuci.2020.08.006.
   Google Scholar
• Sadad T, Rehman A, Munir A, Saba T, Tariq U, Ayesha N, Abbasi R. 2021. Brain tumor detection and multi‐classification using advanced deep learning techniques. Microsc Res Tech. 84(6):1296–1308. doi:10.1002/jemt.23688.
   Google Scholar
• Sathish P, Elango NM. 2017. Exponential cuckoo search algorithm to radial basis neural network for automatic classification in MRI images. Comput Methods Biomech Biomed Eng: Imaging Vis. 7(3):273–285.
   Google Scholar
• Sharan TS, Tripathi S, Sharma S, Sharma N. 2021. Encoder modified U-net and feature pyramid network for multi-class segmentation of cardiac magnetic resonance images. IETE Tech Rev. 1–13. doi:10.1080/02564602.2021.1955760.
   Google Scholar
• Simonyan K, Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition. 2015 Internatinal Conference on Learning Representations (ICLR). doi:10.48550/arXiv.1409.1556.
   Google Scholar
• Sultan HH, Salem NM, Al-Atabany W. 2019. Multi-classification of brain tumor images using deep neural network. IEEE Access. 7:69215–69225. doi:10.1109/ACCESS.2019.2919122.
   Google Scholar
• Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, Lu J. 2019. Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph. 75:34–46. doi:10.1016/j.compmedimag.2019.05.001.
   Google Scholar
• Szandała T. 2020. Review and comparison of commonly used activation functions for deep neural networks. Bio-Inspired Neurocomp Stud Comput Intell. 203–224. doi:10.1007/978-981-15-5495-7_11.
   Google Scholar
• Talo M, Baloglu UB, Yıldırım Ö, Acharya UR. 2019. Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn Syst Res. 54:176–188. doi:10.1016/j.cogsys.2018.12.007.
   Google Scholar
• Toğaçar M, Cömert Z, Ergen B. 2020. Classification of brain MRI using hyper column technique with convolutional neural network and feature selection method. Expert Syst Appl. 149:113274. doi:10.1016/j.eswa.2020.113274.
   Google Scholar
• Tripathi S, Sharan TS, Sharma S, Sharma N. 2021. An augmented deep learning network with noise suppression feature for efficient segmentation of magnetic resonance images. IETE Tech Rev. 1–14.
   Google Scholar
• Tripathi S, Verma A, Sharma N. 2020. Automatic segmentation of brain tumour in MR images using an enhanced deep learning approach. Comput Methods Biomech Biomed Eng: Imaging Vis. 9(2):121–130.
   Google Scholar
• Wu Y, Hatipoglu S, Alonso-Álvarez D, Gatehouse P, Li B, Gao Y, Firmin D, Keegan J, Yang G. 2021. Fast and automated segmentation for the three-directional multi-slice cine myocardial velocity mapping. Diagnostics. 11(2):346. doi:10.3390/diagnostics11020346.
   Google Scholar
• Yamashita R, Nishio M, Do RK, Togashi K. 2018. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 9(4):611–629. doi:10.1007/s13244-018-0639-9.
   Google Scholar
• Yin B, Wang C, Abza F. 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.
   Google Scholar
• Zhou X, Ye Q, Jiang Y, Wang M, Niu Z, Menpes-Smith W, Fang EF, Liu Z, Xia J, Yang G. 2020. Systematic and comprehensive automated ventricle segmentation on ventricle images of the elderly patients: a retrospective study. Front Aging Neurosci. 12. doi:10.3389/fnagi.2020.618538.
   Google Scholar
• Zhou Q, Zhou Z, Chen C, Fan G, Chen G, Heng H … Ji J, Dai Y. 2019. Grading of hepatocellular carcinoma using 3D SE-DenseNet in dynamic enhanced MR images. Comput Biol Med. 107:47–57. doi:10.1016/j.compbiomed.2019.01.026.
   Google Scholar