[1] M. K. Abd-Ellah, A. I. Awad, A. A. M. Khalaf and H. F. A. Hamed, A
review on brain tumor diagnosis from MRI images: Practical
implications, key achievements, and lessons learned, Magnetic
Resonance Imaging 61 (2019), 300-318.
DOI: https://doi.org/10.1016/j.mri.2019.05.028
[2] G. Afendras and M. Markatou, Optimality of training/test size and
resampling effectiveness in cross-validation, Journal of Statistical
Planning and Inference 199 (2019), 286-301.
DOI: https://doi.org/10.1016/j.jspi.2018.07.005
[3] S. Albawi, T. A. Mohammed and S. Al-Zawi, Understanding of a
convolutional neural network, Paper presented at the 2017
International Conference on Engineering and Technology (ICET)
(2017).
DOI: https://doi.org/10.1109/ICEngTechnol.2017.8308186
[4] N. Chakrabarty, Brain MRI images for brain tumor detection,
Kaggle. In. (2019).
[5] R. Chauhan, K. K. Ghanshala and R. C. Joshi, Convolutional neural
network (CNN) for image detection and recognition, Paper presented at
the 2018 First International Conference on Secure Cyber Computing and
Communication (ICSCCC) (2018).
DOI: https://doi.org/10.1109/ICSCCC.2018.8703316
[6] Y. Chen, E. K. Garcia, M. R. Gupta, A. Rahimi and L. Cazzanti,
Similarity-based classification: Concepts and algorithms, Journal of
Machine Learning Research 10(3) (2009), 747-776.
[7] V. Consonni, D. Ballabio and R. Todeschini, Evaluation of model
predictive ability by external validation techniques, Journal of
Chemometrics 24(3-4) (2010), 194-201.
DOI: https://doi.org/10.1002/cem.1290
[8] S. J. S. Gardezi, M. Awais, I. Faye and F. Meriaudeau, Mammogram
classification using deep learning features, Paper presented at the
2017 IEEE International Conference on Signal and Image Processing
Applications (ICSIPA) (2017).
DOI: https://doi.org/10.1109/ICSIPA.2017.8120660
[9] Z. Li, F. Liu, W. Yang, S. Peng and J. Zhou, A survey of
convolutional neural networks: Analysis, applications, and prospects,
IEEE Transactions on Neural Networks and Learning Systems 33(12)
(2021), 6999-7019.
DOI: https://doi.org/10.1109/TNNLS.2021.3084827
[10] J. Liang, Confusion Matrix: Machine Learning 3(4) (2022).
[11] J. Liu, Y. Pan, M. Li, Z. Chen, L. Tang, C. Lu and J. Wang,
Applications of deep learning to MRI images: A survey, Big Data Mining
and Analytics 1(1) (2018), 1-18.
DOI: https://doi.org/10.26599/BDMA.2018.9020001
[12] U. K. Lopes and J. F. Valiati, Pre-trained convolutional neural
networks as feature extractors for tuberculosis detection, Computers
in Biology and Medicine 89 (2017), 135-143.
DOI: https://doi.org/10.1016/j.compbiomed.2017.08.001
[13] M. Masud, M. S. Hossain, H. Alhumyani, S. S. Alshamrani, O.
Cheikhrouhou, S. Ibrahim, Ghulam Muhammad, Amr E. Eldin Rashed and B.
B. Gupta, Pre-trained convolutional neural networks for breast cancer
detection using ultrasound images, ACM Transactions on Internet
Technology 21(4) (2021), 1-17.
DOI: https://doi.org/10.1145/3418355
[14] H. Mohsen, E.-S. A. El-Dahshan, E.-S. M. El-Horbaty and A.-B. M.
Salem, Classification using deep learning neural networks for brain
tumors 3(1) (2018), 68-71.
DOI: https://doi.org/10.1016/j.fcij.2017.12.001
[15] M. A. Naser and M. J. Deen, Brain tumor segmentation and grading
of lower-grade glioma using deep learning in MRI images, Computers in
Biology and Medicine 121 (2020), 103758.
DOI: https://doi.org/10.1016/j.compbiomed.2020.103758
[16] J.-O. Palacio-Niño and F. Berzal, Evaluation metrics for
unsupervised learning algorithms (2019).
[17] J. S. Paul, A. J. Plassard, B. A. Landman and D. Fabbri, Deep
learning for brain tumor classification, Paper presented at the
Medical Imaging 2017: Biomedical Applications in Molecular,
Structural, and Functional Imaging (2017)
DOI: https://doi.org/10.1117/12.2254195
[18] I. Rish, An empirical study of the naive Bayes classifier, Paper
presented at the IJCAI 2001 Workshop on Empirical Methods in
Artificial Intelligence (2001).
[19] K. Simonyan and A. J. Zisserman, Very deep convolutional
networks for large-scale image recognition, (2014).
[20] S. Suthaharan, Support vector machine, In: Machine Learning
Models and Algorithms for Big Data Classification (2016), 207-235.
DOI: https://doi.org/10.1007/978-1-4899-7641-3_9
[21] Gopal S. Tandel, Mainak Biswas, Omprakash G. Kakde, Ashish
Tiwari, Harman S. Suri, Monica Turk, John R. Laird, Christopher K.
Asare, Annabel A. Ankrah, N. N. Khanna, B. K. Madhusudhan, Luca Saba
and Jasjit S. Suri, A review on a deep learning perspective in brain
cancer classification, Cancers 11(1) (2019); Article 111.
DOI: https://doi.org/10.3390/cancers11010111
[22] S. Visa, B. Ramsay, A. L. Ralescu and E. Van Der Knaap,
Confusion matrix-based feature selection, Proceedings of the 22nd
Midwest Artificial Intelligence and Cognitive Science Conference
710(1) (2011), 120-127.
[23] J. Wu, Introduction to convolutional neural networks 5(23)
(2017), 495.