Email this article OPTIMIZING YOLOV9 AND YOLOV10 MODELS FOR BRAIN TUMOR DETECTION: A LEARNING RATE STUDY ON MRI IMAGES
About this article
Received: 03/04/25 Revised: 29/06/25 Published: 29/06/25Authors
1. Ngo Thi Lan, Tay Do University2. Bui Xuan Tung
, Tay Do University
Abstract
This study evaluates the performance of YOLOv9 and YOLOv10 variants in detecting brain tumors in MRI images. We compared four models (YOLOv9t, YOLOv9s, YOLOv10n, YOLOv10s) while optimizing the learning rate parameter to achieve superior performance. Using the Brain_Tumor_Segmentation dataset from Roboflow containing 6,638 images divided into training (80%) and testing (20%) sets. The models were trained with hyperparameters Optimizer = SGD, lr0 = 0.00005, lr0 = 0.0001, Momentum = 0.937, Epoch = 150, Patience = 0, Batchsize = 64 and trained on Kaggle with appropriate GPU configuration. Our findings demonstrate that YOLOv10s with lr0 = 0.0001 achieves the highest overall performance with mAP(50) = 94.3%, mAP(50-95) = 72.3%, Recall = 87.3%, and Precision = 93.9%. Although the YOLOv10s model with lr0 = 0.00005 shows higher accuracy (94.2%), the increased learning rate provides a better balance between detection metrics and convergence speed.
Keywords
Full Text:
PDFReferences
[1] Younis, L. Qiang, C. O. Nyatega, M. J. Adamu, and H. B. Kawuwa, "Brain Tumor Analysis Using Deep Learning and VGG-16 Ensembling Learning Approaches," Applied Sciences, vol. 12, no. 14, 2022, Art. no. 7282.
[2] M. Siar and M. Teshnehlab, "Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm," Proceedings of the 9th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, 2019, pp. 363–365.
[3] M. F. Almufareh, M. Imran, A. Khan, M. Humayun, and M. Asim, "Automated Brain Tumor Segmentation and Classification in MRI Using YOLO-Based Deep Learning," IEEE Access, vol. 12, pp. 16189-16196, 2024.
[4] A. B. Abdusalomov, M. Mukhiddinov, and T. K. Whangbo, "Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging," Cancers, vol. 15, no. 16, 2023, Art. no. 4172.
[5] S. R. Gunasekara, H. N. T. K. Kaldera, and M. B. Dissanayake, "A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring," Journal of Healthcare Engineering, vol. 2021, no. 1, 2021, Art. no. 6695108.
[6] N. Noreen, S. Palaniappan, A. Qayyum, I. Ahmad, M. Imran, and M. Shoaib, "A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor," IEEE Access, vol. 8, pp. 55135-55144, 2020.
[7] K. R. Pedada, B. Rao, K. K. Patro, J. P. Allam, M. M. Jamjoom, and N. A. Samee, "A Novel Approach for Brain Tumour Detection Using Deep Learning Based Technique," Biomedical Signal Processing and Control, vol. 82, 2023, Art. no.104549.
[8] T. Vo and B. N. Thanh, "Polyp Image Segmentation Based on the Recurrent Residual U-Net Improvement Method," HUFLIT Journal of Science, vol. 8, no. 3, pp. 37-37, 2024.
[9] M. Yaseen, "What is YOLOv9: An in-depth exploration of the internal features of the next-generation object detector," arXiv preprint arXiv:2409.07813, pp. 1-10, 2024.
[10] A. Wang, H. Chen, L. Liu, K. Chen, Z. Lin, J. Han, and G. Ding, "YOLOv10: Real-time end-to-end object detection," Advances in Neural Information Processing Systems, vol. 37, pp. 107984-108011, 2024.
[11] Dinesh, "Brain Tumor Segmentation," Roboflow Universe, Roboflow, 2024. [Online]. Available: universe.roboflow.com/dinesh-vjjuw/brain_tumor_segmentation-n0iom/dataset/3. [Accessed Dec. 1, 2024].
DOI: https://doi.org/10.34238/tnu-jst.12474
Refbacks
- There are currently no refbacks.