Authors : Uppalapati Harshitha; Vallamkondu Bhuvitha; Dr. Poornima

Volume/Issue : Volume 10 - 2025, Issue 2 - February

---

Google Scholar : https://tinyurl.com/hxsrpvkb

Scribd : https://tinyurl.com/bdfywhwj

DOI : https://doi.org/10.5281/zenodo.14964479

---

Abstract : This work uses deep learning and machine learning approaches to identify and categorize brain cancers from MRI scans. U-Net is utilized for precise tumor segmentation, YOLOv5 is employed for real-time detection, and Random Forest (RF) and Support Vector Machines (SVM) are employed for tumor type classification. In order to assist doctors, in diagnosing brain tumors more rapidly, the system aims to automate segmentation, detection, and classification, improve diagnosis accuracy, and reduce analysis time. By developing early intervention strategies for brain tumor treatment, this study enhances patient care.

Keywords : Brain Tumor Detection, MRI Image Segmentation, U-Net Architecture,YOLOv5,Random Forest (RF), Support Vector Machine (SVM),Deep Learning, Tumor Classification, Medical Imaging, Real-Time Detection, Image Preprocessing, Artificial Intelligence in Healthcare, Neural Networks, Oncology Diagnostics, Machine Learning

References :

1. S. Bauer, "A survey ofMRI-based medical image analysis for brain tumor studies", Phys. Med. Biol., vol. 58, no. 13, pp. 97-129, 2013.
2. B. Menze, "The multimodal brain tumor image segmentation benchmark(BRATS)", IEEE Trans. Med. Imag., vol. 34, no. 10, pp. 1993-2024, Oct. 2015.
3. Brain tumor segmentation using convolutional neural networks in MRI images. (2016, May 1). IEEE Journals & Magazine | IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/7426413/references#references
4. Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203-211. https://doi.org/10.1038/s41592-020-01008-z
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. https://doi.org/10.1007/978-3-319-24574-4_28
6. Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767.
7. Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018). UNet++: A nested U-Net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 3-11. https://doi.org/10.1007/978-3-030-00889-5_1
8. Litjens, G., Kooi, T., Bejnordi, B. E., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88. https://doi.org/10.1016/j.media.2017.07.005
9. Han, X. (2017). Automatic liver lesion segmentation using a deep convolutional neural network method. arXiv preprint arXiv:1704.07239.
10. Hosseini-Asl, E., Keynton, R., & El-Baz, A. (2016). Alzheimer's disease diagnostics by a 3D deeply supervised adaptable convolutional network. arXiv preprint arXiv:1607.00556.
11. Menze, B. H., Jakab, A., et al. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Transactions on Medical Imaging, 34(10), 1993-2024. https://doi.org/10.1109/TMI.2014.2377694
12. Zhao, X., Wu, Y., et al. (2019). Deep learning for brain tumor classification: A combined 3D convolutional neural network and recurrent neural network approach. Frontiers in Oncology, 9, 842. https://doi.org/10.3389/fonc.2019.00842
13. Bakas, S., et al. (2017). Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data, 4, 170117. https://doi.org/10.1038/sdata.2017.117
14. Pereira, S., Pinto, A., et al. (2016). Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Transactions on Medical Imaging, 35(5), 1240-1251. https://doi.org/10.1109/TMI.2016.2538465
15. Salehi, S. S. M., Erdogmus, D., & Gholipour, A. (2017). Auto-context convolutional neural network (Auto-Net) for brain extraction in magnetic resonance imaging. IEEE Transactions on Medical Imaging, 36(11), 2319-2330. https://doi.org/10.1109/TMI.2017.2738546