Meta-Domain Adaptive Framework for Efficient Diagnostic Assessment of Lung Infection Using CT Radiographs

Computed tomography (CT) scans are widely used for diagnosing lung infections, but manual interpretation is laborious. Artificial intelligence has spurred the development of efficient computer-aided diagnostic (CAD) systems, promising faster and more accurate diagnosis. However, many existing CAD systems lack sufficient cross-data analysis and consequently show suboptimal performance. To address their limitations, we propose a lightweight Meta-Domain Adoptive Segmentation Network (MDA-SN) with adaptive data normalization to enhance infection detection in cross-data analysis. Our optimal network design leverages multi-scale dilated grouped convolution with residual attention to ensure real-time performance and maintain accuracy. We further utilize the model to build a semantic attention-driven retrieval framework, enabling infection ratio quantification and retrieval of relevant CT slices from the database, closely matching the input test sample. Our method 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.