{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T18:08:24Z","timestamp":1790273304149,"version":"4.1.0"},"reference-count":27,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>There is a need to detect and precisely localize brain tumors based on medical imaging to ensure such tumors are diagnosed early and treated with the aim of a successful treatment. More recent developments in the field of deep learning have presented convolution-based and transformer-based object detection models that can be used to achieve high detection rates. We carry out an analogous comparison of YOLOv11s and RF-DETR(small) in brain tumor detection on annotated Magnetic Resonance Imaging (MRI) data on Roboflow in this study. Precision, Recall and mean Average Precision (mAP) along with localization loss measures were used to train and test both models. Findings indicate that YOLOv11s performed better in terms of mAP@50 of 95.2 percent, precision of 93.6 percent, and recall of 91 percent compared to RF-DETR that reported mAP@50 of 94.3 percent, precision of 90.6 percent, and recall of 85 percent. The findings point to the improved performance of YOLOv11s to localize and classify tumor locations in varying MRI slices. YOLOv11s with less convergent dynamic training behavior and RF-DETR(small) with better localization based on more challenging Intersection over Union (IoU) thresholds. The primary challenge was ensuring a fair comparison between two fundamentally different detection paradigms that employ distinct feature-learning mechanisms, pretraining schemes, optimization settings, and computational characteristics. Despite these, our findings highlight the feasibility of integrating advanced object detection models into computer-aided diagnostic tools for robust brain tumor detection in complex MRI data.<\/jats:p>","DOI":"10.1371\/journal.pone.0358483","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:29:41Z","timestamp":1790270981000},"page":"e0358483","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative evaluation of convolutional and transformer-based object detection models for brain tumor localization in MRI"],"prefix":"10.1371","volume":"21","author":[{"given":"Farhan","family":"Naeem","sequence":"first","affiliation":[{"name":"University of Engineering and Technology","place":["Peshawar, Pakistan"],"department":["Department of Computer Systems Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amara","family":"Haroon","sequence":"additional","affiliation":[{"name":"Institute of Kidney Diseases Hayatabad Medical Complex","place":["Peshawar, Pakistan"],"department":["Department of Radiology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasru","family":"Minallah","sequence":"additional","affiliation":[{"name":"University of Engineering and Technology","place":["Peshawar, Pakistan"],"department":["Department of Computer Systems Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8385-7610","authenticated-orcid":true,"given":"Tufail","family":"Ahmad","sequence":"additional","affiliation":[{"name":"University of Engineering and Technology","place":["Peshawar, Pakistan"],"department":["National Center for Bigdata and Cloud Computing"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5712-1565","authenticated-orcid":true,"given":"Shahi","family":"Dost","sequence":"additional","affiliation":[{"name":"University of Liverpool","place":["Liverpool, United Kingdom"],"department":["Department of Mathematical Sciences"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"issue":"3","key":"pone.0358483.ref001","first-page":"1468","article-title":"Brain tumor segmentation using genetic algorithm with SVM classifier","volume":"5","author":"AR Kavitha","year":"2016","journal-title":"Int J Adv Res Electr Electron Instrum Eng"},{"issue":"4","key":"pone.0358483.ref002","doi-asserted-by":"crossref","first-page":"591","DOI":"10.7763\/IJCTE.2010.V2.207","article-title":"An improved implementation of brain tumor detection using segmentation based on hierarchical self organizing map","volume":"2","author":"T Logeswari","year":"2010","journal-title":"Int J Comput Theory Eng"},{"issue":"5","key":"pone.0358483.ref003","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1109\/TCBB.2016.2551745","article-title":"Improve Glioblastoma multiforme prognosis prediction by using feature selection and multiple kernel learning","volume":"13","author":"Y Zhang","year":"2016","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"11","key":"pone.0358483.ref004","doi-asserted-by":"crossref","first-page":"5645","DOI":"10.3390\/app12115645","article-title":"An effective approach to detect and identify brain tumors using transfer learning","volume":"12","author":"N Ullah","year":"2022","journal-title":"Appl Sci"},{"key":"pone.0358483.ref005","doi-asserted-by":"crossref","first-page":"804","DOI":"10.3389\/fnins.2018.00804","article-title":"Glioma grading on conventional MR images: A deep learning study with transfer learning","volume":"12","author":"Y Yang","year":"2018","journal-title":"Front Neurosci"},{"issue":"11","key":"pone.0358483.ref006","doi-asserted-by":"crossref","first-page":"5526","DOI":"10.1016\/j.eswa.2014.01.021","article-title":"Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm","volume":"41","author":"E-SA El-Dahshan","year":"2014","journal-title":"Expert Syst Appl"},{"key":"pone.0358483.ref007","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/978-981-10-9035-6_33","article-title":"Brain Tumor Classification Using Convolutional Neural Network.","volume-title":"World Congress on Medical Physics and Biomedical Engineering 2018","author":"N Abiwinanda","year":"2019"},{"key":"pone.0358483.ref008","unstructured":"Jocher G, Qiu J. Ultralytics YOLO11 [GitHub repository]. 2024. Available from: https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"pone.0358483.ref009","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","article-title":"End-to-End Object Detection with Transformers.","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020"},{"key":"pone.0358483.ref010","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2024.3359418","article-title":"Automated brain tumor segmentation and classification using YOLOv5 and YOLOv7","volume":"12","author":"MF Almufareh","year":"2024","journal-title":"IEEE Access"},{"key":"pone.0358483.ref011","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1109\/ICSSAS66150.2025.11081289","article-title":"Adaptive Deep Neural Architecture for Brain Tumor Detection Using YOLO and Transfer Learning.","volume-title":"2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)","author":"K Vinayakan","year":"2025"},{"issue":"2","key":"pone.0358483.ref012","first-page":"145","article-title":"Automated brain tumor detection using YOLOv11 deep learning model","volume":"16","author":"S Kumar","year":"2024","journal-title":"Asian J Pharm Res Health Care"},{"issue":"41","key":"pone.0358483.ref013","first-page":"41","article-title":"Lightweight brain tumor detection using YOLOv4-Tiny with transfer learning","volume":"182","author":"FJP Montalbo","year":"2020","journal-title":"Int J Comput Appl"},{"key":"pone.0358483.ref014","article-title":"Comparative analysis of YOLOv9, YOLOv10, and YOLOv11 for brain tumor detection","author":"A Ali","year":"2025","journal-title":"Adv Brain Biomed Data Model"},{"issue":"3","key":"pone.0358483.ref015","first-page":"63","article-title":"YOLO-based automated meningioma firmness detection using MRI","volume":"12","author":"NF Alhussainan","year":"2024","journal-title":"Computers"},{"key":"pone.0358483.ref016","first-page":"54321","article-title":"Automatic brain tumor detection using YOLOv4-Tiny and image preprocessing","volume":"12","author":"M Rahimi","year":"2024","journal-title":"IEEE Access"},{"key":"pone.0358483.ref017","article-title":"Enhanced YOLOv11n for brain tumor detection using GhostConv, OREPA, and EMA","volume":"16","author":"J Chen","year":"2025","journal-title":"Front Neurol"},{"key":"pone.0358483.ref018","article-title":"YOLOv11 with TwinFormer and SCFStage for Brain Tumor Detection","author":"S Raju","year":"2025","journal-title":"Alexandria Eng J"},{"issue":"16","key":"pone.0358483.ref019","first-page":"4043","article-title":"YOLOv7-based multi-scale feature fusion for brain tumor detection","volume":"15","author":"B Abdusalomov","year":"2023","journal-title":"Cancers"},{"issue":"1","key":"pone.0358483.ref020","article-title":"Hybrid YOLOv11-SAM model for MRI-based brain tumor segmentation","volume":"21","author":"D Jeyaraj","year":"2025","journal-title":"Current Medical Imaging"},{"key":"pone.0358483.ref021","unstructured":"Research-jz5sv. Brain Tumor MRI ycidy-anq3s Dataset [Internet]. Database: Roboflow Universe. 2024. Available from: https:\/\/universe.roboflow.com\/research-jz5sv\/brain-tumor-mri-ycidy-anq3s"},{"key":"pone.0358483.ref022","unstructured":"Bochkovskiy A, Wang CY, Liao HYM. YOLOv4: Optimal speed and accuracy of object detection [Preprint]. arXiv:2004.10934; 2020. Available from: https:\/\/arxiv.org\/abs\/2004.10934"},{"key":"pone.0358483.ref023","unstructured":"Zhang S, Chi C, Yao Y, Lei Z, Li SZ. Distribution-based object detection: A new perspective for bounding box regression. In: Proceedings of the European Conference on Computer Vision (ECCV). 2020."},{"key":"pone.0358483.ref024","doi-asserted-by":"crossref","unstructured":"Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S. Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"pone.0358483.ref025","unstructured":"Ultralytics. YOLOv11: An Overview of the Key Architectural Enhancements [Preprint]. arXiv:2410.17725. 2024. Available from: https:\/\/arxiv.org\/abs\/2410.17725"},{"key":"pone.0358483.ref026","unstructured":"Roboflow. rf-detr: Roboflow implementation of Real-time DEtection TRansformer [GitHub repository]. 2023. Available from: https:\/\/github.com\/roboflow\/rf-detr"},{"key":"pone.0358483.ref027","unstructured":"Zhu X, Su W, Lu L, Li B, Wang X, Dai J. Deformable DETR: Deformable transformers for end-to-end object detection. In: Proceedings of the International Conference on Learning Representations (ICLR). 2021."}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0358483","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:29:54Z","timestamp":1790270994000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0358483"}},"subtitle":[],"editor":[{"given":"Wei","family":"Song","sequence":"first","affiliation":[{"name":"Northeastern University","place":["CHINA"]}],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"references-count":27,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9,24]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0358483","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]}}}