Advances in Machine Learning for Brain Tumor Diagnosis: Review and Research Outlook

1. Ankush, Meenakshi Arora and Rohini Sharma, “Enhanced Brain Tumour Detection Using Anisotropic Diffusion and Morphological
Operations”, Best journal of innovation in science, research and development, Vol.3 Issue 4 , 2024, pp. 717-724.
2. Louis, D. N., Perry, A., Reifenberger, G., von Deimling, A., Figarella-Branger, D., Cavenee, W. K., ... & Ellison, D. W. (2016). The 2016
World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathologica, 131(6), 803–
820.
3. Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). Brain tumor segmentation using convolutional neural networks in MRI images.
IEEE Transactions on Medical Imaging, 35(5), 1240–1251.
4. Hossain, M. S., & Muhammad, G. (2019). Brain tumor detection using multi-modal imaging and 3D convolutional neural network.
Journal of Computational Science, 30, 1–9.
5. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International
Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 234–241.
6. Islam, M., Zhang, Y., & Ren, H. (2020). Brain tumor classification using deep learning features and SVM. Biomedical Signal Processing
and Control, 68, 102-107.
7. Wang, G., Li, W., Ourselin, S., & Vercauteren, T. (2018). Automatic brain tumor segmentation using cascaded anisotropic convolutional
neural networks. Medical Image Analysis, 44, 1–13.
8. Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J. N., Wu, Z., & Ding, X. (2020). Embracing imperfect datasets: A review of deep learning
solutions for medical image segmentation. Medical Image Analysis, 63, 101693.
9. Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019). Causability and explainability of artificial intelligence in medicine.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4), e1312.
10. Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2020). Federated learning in medicine: facilitating multi-institutional
collaborations without sharing patient data. Scientific Reports, 10(1), 12598.
11. Mehta, R., Majumdar, A., & Sivaswamy, J. (2019). BrainSegNet: A convolutional neural network architecture for automated segmentation
of human brain structures. Journal of Medical Imaging, 6(1), 014001.
12. Ankush, Meenakshi Arora, Rohini Sharma, “Machine Learning Based Brain Tumor Detection and Classification: Review and Future
Perceptions,” International Journal of Advanced Research in Education and Technology (IJARETY), Vol. 11, Issue 2, March 2024, pp.
595-602.
13. M. Shakeri et al., “Sub-cortical brain structure segmentation using F-CNN’s,” in IEEE 13th Int. Symp. on Biomedical Imaging (ISB), pp.
269–272 (2016).
14. F. Milletari et al., “Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound,” arXiv preprint arXiv:
1601.07014 (2016).
15. S. Bao and A. C. Chung, “Multi-scale structured CNN with label consistency for brain MR image segmentation,” Comput. Methods
Biomech. Biomed. Eng.: Imaging Visualization, 4, 1–5 (2016).
16. Iftikhar, S., Anjum, N., Siddiqui, A.B. et al. Explainable CNN for brain tumor detection and classification through XAI based key features
identification. Brain Inf. 12, 10 (2025).
17. T. Chapelle et al., “Choosing multiple parameters for SVM classification,” Machine Learning, 2002.
18. Tandel, G. S., Biswas, M., Kakde, O. G., & Suri, J. S. (2020). "A review on a deep learning perspective in brain cancer classification."
Diagnostics, 10(11), 863.
19. Paul, J., Sajnani, N., & Shah, K. (2017). "Brain tumor detection using image processing: A survey." International Journal of Advanced
Research in Computer and Communication Engineering, 6(4), 434–437.
20. Afshar, P., Mohammadi, A., & Plataniotis, K. N. (2019). “Brain tumor type classification via capsule networks.” International Journal
of Medical Imaging.
21. Chaddad, A. (2015). “Automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models.”
International Journal of Biomedical Imaging.
22. Afshar, P., Mohammadi, A., & Plataniotis, K. N. (2019)
“Brain tumor type classification via capsule networks.”
23. Yang, G., et al. (2017) “Automatic brain tumor segmentation using a cascaded convolutional and recurrent neural network.”
International MICCAI Workshop.
24. Yang, G., et al. (2017), “Automatic Brain Tumor Segmentation Using Cascaded CNN and LSTM Networks” MICCAI Workshop.
25. Bowles, C. et al. (2018), “GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks” MICCAI.
26. Baur, C. et al. (2019). “Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study.”
27. Bach, S. et al. (2015). "On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation." Applied
LRP to visualize classifier decisions for medical images, including brain MRIs.