YOLOv11-MFF: A multi-scale frequency-adaptive fusion network for enhanced CXR anomaly detection
Li Guan, Ruting Zhang, Yi Zhao
TL;DR
This paper introduces YOLOv11-MFF, a new network for detecting anomalies in chest X-rays that improves detection accuracy by adapting to lesion characteristics.
Contribution
The novel Frequency-Adaptive Hybrid Gate and Multi Scale Parallel Large Convolution block enhance lesion detection in chest X-rays.
Findings
YOLOv11-MFF achieves a precision of 48.2% and recall of 42.5% on the VinDr-CXR dataset.
The model outperforms state-of-the-art models with an [email protected] of 41.5% and [email protected]:0.95 of 22.6%.
Abstract
Chest X-ray (CXR) represents one of the most widely utilized clinical diagnostic tools for thoracic diseases. Nevertheless, computer-aided diagnosis based on chest radiographs still faces considerable challenges in anomaly detection. Certain lesions in CXRs exhibit subtle radiographic characteristics with ambiguous boundaries, low pixel occupancy, and weak contrast. While existing studies primarily focus on improving multi-scale feature fusion, they frequently overlook complications arising from background noise and varied lesion morphology. This study introduces YOLOv11-MFF, an enhanced YOLOv11 network with three key innovations. Specifically, a novel Frequency-Adaptive Hybrid Gate (FAHG) is developed to improve contrast differentiation between lesions and background. A Multi Scale Parallel Large Convolution (MSPLC) block is designed and integrated with the original C3k2 module to…
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Taxonomy
TopicsCOVID-19 diagnosis using AI · Lung Cancer Diagnosis and Treatment