Meta-domain adaptive framework for efficient diagnostic assessment of lung infection using CT radiographs

Abstract

Computed Tomography (CT) scans are widely used to diagnose lung infections; however, manual interpretation is labor-intensive. Artificial intelligence has accelerated the development of computer-aided diagnostic (CAD) systems, allowing faster and more accurate diagnosis. Nevertheless, many existing CAD systems lack robust cross-dataset generalization and interpretability, limiting their reliability and resulting in suboptimal diagnostic performance. To address these limitations, we propose a semantic attention-driven retrieval framework based on a lightweight Meta-Domain Adaptive Segmentation Network (MDA-SN) with an adaptive data normalization strategy to enhance infection detection in cross-dataset analysis. This framework quantifies infection ratios and retrieves relevant CT slices from the database, closely matching the input test sample to further support medical experts in making more accurate diagnostic decisions. The MDA-SN design leverages multi-scale dilated grouped convolution with residual attention to ensure real-time performance while maintaining accuracy. Our framework achieved an average cross-dataset performance of 75.93% Dice index and 67.42% Intersection over Union, surpassing state-of-the-art methods by 3.32% and 3.28%, respectively. Additionally, it achieves real-time execution, processing an average of 29 slices per second, due to its significantly reduced number of training parameters, approximately 70% fewer than its closest competitor. The implementation and materials are available at our GitHub repository: https://github.com/Owais-CodeHub/MDA-SN .