Real-time medical lesion screening: accurate and rapid detectors

Brain tumors are highly lethal, representing 85–90% of all primary central nervous system (CNS) tumors. Magnetic resonance imaging (MRI) images are employed to identify and assess brain tumors. However, this process has historically relied heavily on the expertise of medical professionals and necessitated the involvement of a substantial number of personnel. To optimize the allocation of medical resources and improve diagnostic efficiency, this work proposes a DETR-based RPC–DETR model that utilizes the Transformer. We conducted comparative experiments using RPC–DETR with other traditional detectors on the same equipment to test the performance exhibited by the model with equal computational resources. Tested on the Br35H brain tumor dataset with 701 MRI images (500 training sets and 201 test sets), RPC–DETR surpasses the YOLO models in accuracy while utilizing fewer parameters. This advancement ensures more reliable and faster diagnosis. RPC–DETR achieves a 96% mAP with just 14M parameters, offering high accuracy in brain tumor detection with a lighter model, making it easier to implement in various medical settings.