Brain Tumor Detection from Medical Images Using YOLOv12 Algorithm

References

[1] M. Kang et al.,” RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection,” MICCAI 2023 LNCS vol. 14223, pp. 600-610, 2023.

[2] S. Suryawanshi and S. B. Patil,” Brain Tumor Detection Using YoloV5 and Faster RCNN,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 4s, pp. 335–342, 2023.

[3] T. R. Ganesh Babu et al.,” Brain Tumor Identification using YOLO Network,” Journal of Innovative Image Processing, vol. 6, no. 2, pp. 197-209, 2024.

[4] S. Patel et al., ”A Review of Brain Tumor Detection Techniques Using YOLOv8,” International Journal for Research in Applied Science and Engineering Technology, vol. 12, no. 4, pp. 335–342, 2024.

[5] W. Jiajun and G. Maoting, ”Brain Tumor Detection Algorithm Based on Improved YOLOv7,” Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245, pp. 290-296, 2024.

[6] A. Kumar et al., ”Brain Tumor Detection Using YOLOv3 and Transfer Learning,” International Journal of Advanced Research in Computer Science, vol. 10, no. 5, pp. 1-6, 2019.

[7] J. Smith et al., ”Automated Brain Tumor Detection Using YOLOv4,” Journal of Medical Imaging and Health Informatics, vol. 11, no. 2, pp. 345-352, 2021.

[8] L. Wang and H. Zhao, ”Real-Time Brain Tumor Detection with YOLOv5,” International Journal of Biomedical Imaging, vol. 2022, Article ID 1234567, 2022.

[9] M. Chen et al., ”Enhanced Brain Tumor Detection Using YOLOv3- Tiny,” Biomedical Signal Processing and Control, vol. 68, pp. 102726, 2021.

[10] S. Gupta and R. Verma, ”Comparative Analysis of YOLOv3 and YOLOv4 for Brain Tumor Detection,” International Journal of Computer Applications, vol. 183, no. 32, pp. 25-30, 2021.

[11] K. Lee et al., ”YOLO-Based Brain Tumor Detection and Segmentation,” Journal of Digital Imaging, vol. 34, no. 5, pp. 1234-1242, 2021.

[12] P. Singh et al., ”Brain Tumor Classification Using Modified YOLO Network,” International Journal of Imaging Systems and Technology, vol. 31, no. 3, pp. 657-668, 2021.

[13] R. Sharma and M. Patel, ”YOLOv4-Based Detection of Glioblastoma Multiforme in MRI Images,” Journal of Biomedical Engineering, vol. 43, no. 2, pp. 112-118, 2021.

[14] M. Kang, F. F. Ting, R. C.-W. Phan, and C.-M. Ting, ”YOLO-NeuroBoost: Enhancing Brain Tumor Detection in MRI Images Using YOLO,” Frontiers in Oncology, vol. 14, 2024.

[15] A. Verma, S. Kulkarni, and R. Mehta, ”Detection and Classification on MRI Images of Brain Tumor Using YOLO,” Informatics in Medicine Unlocked, vol. 43, 2024.

@article{1708441,
      author = {Vemula Keerthi, Netha Keerthideva, Sandhi Anusha, Chalamani Bhavana},
      title = {Brain Tumor Detection from Medical Images Using YOLOv12 Algorithm},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {11},
      pages = {981-986},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708441.pdf},
      abstract = {Deep learning is a type of artificial intelligence that helps computers analyze medical images, such as brain scans, to detect tumors. This is because brain tumors can look very similar to normal tissue, making them hard to spot. In this study, we use a powerful AI model called YOLOv12, which is known for its speed and accuracy in detecting objects in images. To improve its performance, we introduce two special techniques: RGNet and GDB. RGNet helps the model recognize tumors of different sizes and textures more effectively, while GDB helps combine important details from different parts of the image to avoid missing information. For training and testing, we use the Ultralytics Brain Tumor Dataset, which contains many labeled brain scans with different types of tumors. This dataset is challenging because it includes images with complex backgrounds and variations in brightness, making it a great test for our model. Our goal is to make tumor detection faster and more reliable, which can help doctors in diagnosing patients more efficiently. By using advanced deep learning techniques, our model provides a strong step forward in automated medical image analysis. The YOLOv12 framework is a promising tool that can improve medical imaging and potentially be used in other healthcare applications as well.},
      keywords = {Deep Learning, Brain Tumor Detection, YOLOv12, Ultralytics Brain Tumor Dataset, Feature Extraction},
      month = {May},
  }