LungX: A Hybrid EfficientNet-Vision Transformer Architecture with Multi-Scale Attention for Accurate Pneumonia Detection
This work addresses timely diagnosis of pneumonia, a leading global cause of mortality, but is incremental as it builds on existing methods for a specific medical imaging task.
The paper tackled pneumonia detection by introducing LungX, a hybrid architecture combining EfficientNet, CBAM attention, and Vision Transformer, achieving state-of-the-art performance with 86.5% accuracy and 0.943 AUC on chest X-ray datasets.
Pneumonia remains a leading global cause of mortality where timely diagnosis is critical. We introduce LungX, a novel hybrid architecture combining EfficientNet's multi-scale features, CBAM attention mechanisms, and Vision Transformer's global context modeling for enhanced pneumonia detection. Evaluated on 20,000 curated chest X-rays from RSNA and CheXpert, LungX achieves state-of-the-art performance (86.5 percent accuracy, 0.943 AUC), representing a 6.7 percent AUC improvement over EfficientNet-B0 baselines. Visual analysis demonstrates superior lesion localization through interpretable attention maps. Future directions include multi-center validation and architectural optimizations targeting 88 percent accuracy for clinical deployment as an AI diagnostic aid.