Research Article
A Chest X-ray Pneumonia Recognition Model Based on Quality Perception
@INPROCEEDINGS{10.4108/eai.22-5-2026.2365104, author={Zhiyuan Li}, title={A Chest X-ray Pneumonia Recognition Model Based on Quality Perception}, proceedings={Proceedings of the 4th International Conference on Image, Algorithms, and Artificial Intelligence, ICIAAI 2026, 22-24 May 2026, Singapore, Singapore}, publisher={EAI}, proceedings_a={ICIAAI}, year={2026}, month={8}, keywords={Pneumonia identification Chest X-ray Quality perception learning Model reliability Medical image analysis}, doi={10.4108/eai.22-5-2026.2365104} }- Zhiyuan Li
Year: 2026
A Chest X-ray Pneumonia Recognition Model Based on Quality Perception
ICIAAI
EAI
DOI: 10.4108/eai.22-5-2026.2365104
Abstract
Variations in chest X-ray quality commonly lead to model performance degradation in clinical settings. To address this problem, this paper introduces the QualityAware Pneumonia Recognition (QAPN) model. Built on a ConvNeXt backbone, QAPN incorporates a lightweight quality-assessment branch to characterize image degradation and employs a Quality-Guided Attention (QGA) mechanism to adaptively reweight lesion features. Experimental results show that QAPN outperforms all baseline models by 2.7– 15.0 percentage points in terms of F1 score under various low-quality conditions, including image noise and improper exposure. More importantly, the model automatically reduces its prediction confidence for poor-quality images, thereby improving the robustness and safety of computer-aided diagnosis in complex clinical environments.
