RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection
This work addresses brain tumor detection for medical imaging applications, but it is incremental as it builds on existing YOLO frameworks.
The authors tackled brain tumor detection by proposing RCS-YOLO, a novel YOLO architecture that improves precision by 1% and inference speed by 60% compared to YOLOv7, achieving 114.8 FPS on the Br35H dataset.
With an excellent balance between speed and accuracy, cutting-edge YOLO frameworks have become one of the most efficient algorithms for object detection. However, the performance of using YOLO networks is scarcely investigated in brain tumor detection. We propose a novel YOLO architecture with Reparameterized Convolution based on channel Shuffle (RCS-YOLO). We present RCS and a One-Shot Aggregation of RCS (RCS-OSA), which link feature cascade and computation efficiency to extract richer information and reduce time consumption. Experimental results on the brain tumor dataset Br35H show that the proposed model surpasses YOLOv6, YOLOv7, and YOLOv8 in speed and accuracy. Notably, compared with YOLOv7, the precision of RCS-YOLO improves by 1%, and the inference speed by 60% at 114.8 images detected per second (FPS). Our proposed RCS-YOLO achieves state-of-the-art performance on the brain tumor detection task. The code is available at https://github.com/mkang315/RCS-YOLO.