CGF-DETR: Cross-Gated Fusion DETR for Enhanced Pneumonia Detection in Chest X-rays

TL;DR

This paper introduces CGF-DETR, a novel transformer-based model for pneumonia detection in chest X-rays, combining multi-scale feature extraction and efficient feature fusion to improve accuracy while maintaining real-time performance.

Contribution

The paper proposes XFABlock, SPGA, and GCFC3 modules, enhancing feature extraction and fusion in a transformer architecture tailored for medical imaging detection tasks.

Findings

Achieves 82.2% mAP@0.5 on RSNA dataset

Outperforms baseline RT-DETR-l by 3.7% mAP

Maintains 48.1 FPS inference speed

Abstract

Pneumonia remains a leading cause of morbidity and mortality worldwide, necessitating accurate and efficient automated detection systems. While recent transformer-based detectors like RT-DETR have shown promise in object detection tasks, their application to medical imaging, particularly pneumonia detection in chest X-rays, remains underexplored. This paper presents CGF-DETR, an enhanced real-time detection transformer specifically designed for pneumonia detection. We introduce XFABlock in the backbone to improve multi-scale feature extraction through convolutional attention mechanisms integrated with CSP architecture. To achieve efficient feature aggregation, we propose SPGA module that replaces standard multi-head attention with dynamic gating mechanisms and single-head self-attention. Additionally, GCFC3 is designed for the neck to enhance feature representation through multi-path convolution fusion while maintaining real-time performance via structural re-parameterization. Extensive experiments on the RSNA Pneumonia Detection dataset demonstrate that CGF-DETR achieves 82.2% mAP@0.5, outperforming the baseline RT-DETR-l by 3.7% while maintaining comparable inference speed at 48.1 FPS. Our ablation studies confirm that each proposed module contributes meaningfully to the overall performance improvement, with the complete model achieving 50.4% mAP@[0.5:0.95]