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Jue, M. Mushtaq, and M. U. Mushtaq, \u201cBrain tumor\nclassification in mri image using convolutional neural network,\u201d Mathematical Biosciences and Engineering, 2021."},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"A. Pashaei, H. Sajedi, and N. Jazayeri, \u201cBrain tumor classification via\nconvolutional neural network and extreme learning machines,\u201d in 2018\n8th International conference on computer and knowledge engineering\n(ICCKE), pp. 314\u2013319, IEEE, 2018.","DOI":"10.1109\/ICCKE.2018.8566571"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"K. Muhammad, S. Khan, J. Del Ser, and V. H. C. De Albuquerque,\n\u201cDeep learning for multigrade brain tumor classification in smart\nhealthcare systems: A prospective survey,\u201d IEEE Transactions on Neural\nNetworks and Learning Systems, vol. 32, no. 2, pp. 507\u2013522, 2020.","DOI":"10.1109\/TNNLS.2020.2995800"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"C. Narmatha, S. M. 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