AS-YOLO: A Novel YOLO Model with Multi-Scale Feature Fusion for Intracranial Aneurysm Recognition

Abstract

Intracranial aneurysm is a common clinical disease that seriously endangers the health of patients. In view of the shortcomings of existing intracranial aneurysm recognition methods in dealing with complex aneurysm morphologies, varying sizes, as well as multi-scale feature extraction and lightweight deployment, this study introduces an intracranial aneurysm detection framework, AS-YOLO, which is designed to enhance recognition precision while ensuring compatibility with lightweight device deployment. Built on the YOLOv8n backbone, this approach incorporates a cascaded enhancement module to refine representation learning across scales. In addition, a multi-stage fusion strategy was employed to facilitate efficient integration of cross-scale semantic features. Then, the detection head was improved by proposing an efficient depthwise separable convolutional aggregation detection head. This modification significantly lowers both the parameter count and computational burden without compromising recognition precision. Finally, the SIoU-based regression loss was employed, enhancing the bounding box alignment and boosting overall detection performance. Compared with the original YOLOv8, the proposed solution achieves higher recognition precision for aneurysm detection—boosting [email protected] by 8.7% and [email protected]:0.95 by 4.96%. Meanwhile, the overall model complexity is effectively reduced, with a parameter count reduction of 8.21%. Incorporating multi-scale representation fusion and lightweight design, the introduced model maintains high detection accuracy and exhibits strong adaptability in environments with limited computational resources, including mobile health applications.