STAR-YOLO: A High-Accuracy and Ultra-Lightweight Method for Brain Tumor Detection - Liyan Sun; Linxuan Zheng; Zhiguo Xiao; Yi Xin; Linqing Jiang (IEEE Access)
Brain tumors are among the deadliest malignant neoplasms worldwide, and early detection is critical for improving treatment outcomes and patient prognosis. Magnetic resonance imaging (MRI) plays a central role in the clinical diagnosis of brain tumors due to its exceptional ability to visualize brain structures and detect tumors. However, traditional MRI detection models for brain tumors often struggle to balance high accuracy with a lightweight design. To address this challenge, this study proposes a novel model—STAR-YOLO—based on the optimization of YOLOv8n. The model refines the backbone network and enhances the C2f module within the neck network. It also integrates SimAM, a parameter-free attention mechanism, and utilizes ShapeIoU as the localization loss function. These innovations enable STAR-YOLO to achieve both high accuracy and a lightweight design. Experimental results on the Br35H public dataset show that STAR-YOLO outperforms existing models, achieving outstanding scores of 0.937 in Precision, 0.852 in Recall, and 0.642 in mAP50–95. Additionally, the model contains only 2.01 million parameters and has a computational complexity of 6.1 GFLOPs, significantly lower than other models. STAR-YOLO accomplishes a lightweight design while guaranteeing high detection accuracy, prominently demonstrating its immense potential in the diagnosis of clinical brain tumors, particularly in circumstances with constrained computing resources.