Edge-Guided Deep Learning for Enhanced Pneumonia Detection in Radiographs
TL;DR
A new Pneumonia Multi-Scale Attention Network that synergistically integrates multiscale feature extraction, channel-wise attention mechanisms, and a novel edge-aware loss (EAL) function to improve pneumonia classification accuracy and interpretability is introduced.
Abstract
Abstract Pneumonia diagnosis via chest X-rays is critical for effective clinical intervention, yet conventional approaches often fail to capture subtle pathologies. This article introduces a new Pneumonia Multi-Scale Attention Network (PMSAN). This deep learning framework synergistically integrates multiscale feature extraction, channel-wise attention mechanisms, and a novel edge-aware loss (EAL) function to improve pneumonia classification accuracy and interpretability. Extensive experiments on a Kermany pediatric chest X-ray data set (5,856 images) demonstrate that PMSAN, when trained with the EAL, achieves a test set accuracy of 96.4%, precision of 97.9%, recall of 97.2%, F1-score of 97.6%, and an area under the curve (AUC) of 0.988. In addition, fivefold cross-validation shows consistent performance with an accuracy of 96.4% ± 0.5% and AUC of 0.988 ± 0.003, outperforming baseline models such as ResNet18 and VGG16. The model's enhanced interpretability is supported by visualizations including receiver operating characteristic curves, attention maps, and edge maps.