1. Alizadeh L, Gorizan A, Akbari Dana M, Ghaemi A. Immunotherapy of Glioblastoma Multiforme Tumors: From Basic to Clinical Trial Studies. The Neuroscience Journal of Shefaye Khatam. 2015; 3(2): 77-84. [ DOI:10.18869/acadpub.shefa.3.2.77] 2. Seyed Abbasi M, Zakariaee S, Rahimiforoushani A. Estimation of Hemodynamic Response Function in the Brain and Brain Tumors: Comparison of Inverse Logistic and Canonical Hemodynamic Response Function Models. The Neuroscience Journal of Shefaye Khatam. 2018; 6(3): 1-9. [ DOI:10.29252/shefa.6.3.1] 3. Jalali Kondori B, Rahimian E, Asadi MH, Tahsini MR. Magnetic Resonance Tractography and its Clinical Applications. The Neuroscience Journal of Shefaye Khatam. 2014; 2(4): 71-8. [ DOI:10.18869/acadpub.shefa.2.4.71] 4. Kosiorowska P, Pasieka K, Perenc H, Majka K, Krawczyk K, Pędras M, et al. Overview of medical analysis capabilities in radiology of current Artificial Intelligence models. Quality in Sport. 2024; 20:5 3933. [ DOI:10.12775/QS.2024.20.53933] 5. Samifar F, Samifar S, Vafaee F, Gorji A. The Use of Artificial Intelligence in the Evaluation of Multiple Sclerosis Brain Lesions Through the Processing of MRI Images. The Neuroscience Journal of Shefaye Khatam. 2023; 12(1): 67-84. [ DOI:10.61186/shefa.12.1.67] 6. Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017; 39(6): 1137-49. [ DOI:10.1109/TPAMI.2016.2577031] 7. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, et al, editors. SSD: Single Shot MultiBox Detector 2016; Cham: Springer International Publishing. [ DOI:10.1007/978-3-319-46448-0_2] 8. Redmon J, Divvala S, Girshick R, Farhadi A, editors. You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016; 27-30. [ DOI:10.1109/CVPR.2016.91] 9. Farajpour H, Banimohamad-Shotorbani B, Rafiei-Baharloo M, Lotfi H. Application of Artificial Intelligence in Regenerative Medicine. The Neuroscience Journal of Shefaye Khatam. 2023; 11(4): 94-107. [ DOI:10.61186/shefa.11.4.94] 10. Qin Y, He J. YOLOv1 to YOLOv10: A Comprehensive Review of YOLO Variants and Their Application in Medical Image Detection. 11. Martucci M, Russo R, Schimperna F, D'Apolito G, Panfili M, Grimaldi A, et al. Magnetic resonance imaging of primary adult brain tumors: state of the art and future perspectives. Biomedicines. 2023; 11(2): 364. [ DOI:10.3390/biomedicines11020364] 12. Zhou SK, Greenspan H, Davatzikos C, Duncan JS, Van Ginneken B, Madabhushi A, et al. A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. Proceedings of the IEEE. 2021; 109(5): 820-38. [ DOI:10.1109/JPROC.2021.3054390] 13. Hafizović L, Čaušević A, Deumić A, Bećirović LS, Pokvić LG, Badnjević A, editors. The use of artificial intelligence in diagnostic medical imaging: systematic literature review. 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE); 2021: IEEE. [ DOI:10.1109/BIBE52308.2021.9635307] 14. Redmon J, Farhadi A, editors. YOLO9000: Better, Faster, Stronger. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017; 21-26. [ DOI:10.1109/CVPR.2017.690] 15. Redmon J. Yolov3: An incremental improvement. arXiv preprint arXiv: 180402767. 2018. 16. Bochkovskiy A, Wang C-Y, Liao H-YM. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv: 200410934. 2020. 17. Glenn Jocher. YOLOv5 by Ultralytics https://github.com/ultralytics2020 [Available from: https://github.com/ultralytics/yolov5. 18. Li C, Li L, Jiang H, Weng K, Geng Y, Li L, et al. YOLOv6: A single-stage object detection framework for industrial applications. arXiv preprint arXiv: 220902976. 2022. 19. Wang C-Y, Bochkovskiy A, Liao H-YM, editors. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; 2023. [ DOI:10.1109/CVPR52729.2023.00721] 20. Glenn Jocher, Qiu Jing, Chaurasia A. Ultralytics YOLO https://github.com/ultralytics/ultralytics2023 [Available from: https://ultralytics.com. 21. Wang C-Y, Yeh I-H, Liao H-YM. YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. arXiv preprint arXiv: 240213616. 2024. [ DOI:10.1007/978-3-031-72751-1_1] 22. Hussain M. YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection. Machines. 2023; 11(7): 677. [ DOI:10.3390/machines11070677] 23. Ultralytics. Yolov8 anchor-free bounding box prediction - issue 189 [Available from: https://github.com/ultralytics/ultralytics/issues/189. 24. Roboflow. Tumor Detection Computer Vision Project [Available from: https://universe.roboflow.com/celebal-p3kbm/tumor-detection-j9mqs.
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