<p>Per-class image count.</p>

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.