SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosis
Journal:
arXiv
Published Date:
Jan 7, 2025
Abstract
Brain tumors can lead to neurological dysfunction, cognitive and
psychological changes, increased intracranial pressure, and seizures, posing
significant risks to health. The You Only Look Once (YOLO) series has shown
superior accuracy in medical imaging object detection. This paper presents a
novel SCC-YOLO architecture that integrates the SCConv module into YOLOv9. The
SCConv module optimizes convolutional efficiency by reducing spatial and
channel redundancy, enhancing image feature learning. We examine the effects of
different attention mechanisms with YOLOv9 for brain tumor detection using the
Br35H dataset and our custom dataset (Brain_Tumor_Dataset). Results indicate
that SCC-YOLO improved mAP50 by 0.3% on the Br35H dataset and by 0.5% on our
custom dataset compared to YOLOv9. SCC-YOLO achieves state-of-the-art
performance in brain tumor detection.
