LungX: A Hybrid EfficientNet-Vision Transformer Architecture with Multi-Scale Attention for Accurate Pneumonia Detection

TL;DR

LungX is a hybrid deep learning model combining EfficientNet, CBAM attention, and Vision Transformers, achieving state-of-the-art accuracy in pneumonia detection from chest X-rays with interpretable lesion localization.

Contribution

The paper introduces LungX, a novel hybrid architecture that integrates multi-scale features, attention mechanisms, and global context modeling for improved pneumonia diagnosis.

Findings

Achieves 86.5% accuracy and 0.943 AUC on chest X-ray datasets.

Outperforms EfficientNet-B0 baseline by 6.7% AUC.

Provides interpretable attention maps for lesion localization.

Abstract

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.