A Panoramic Survey of CNN-based Methods for Lung CT/CXR and Clinical Integration: Current Work, Methods, Results, Strengths, Limitations, and Practical Recommendations

This study systematically reviews the past five years of convolutional neural network (CNN) research in pulmonary imaging for screening, triage, and follow-up. Guided by PRISMA, we analyzed English-language studies (2020–2025) from PubMed/MEDLINE, IEEE Xplore, Scopus, and Google Scholar, focusing on CT/LDCT and chest X-ray (CXR) applications for detection, segmentation, and prognosis. Data extraction was standardized across datasets, preprocessing, model architectures, validation strategies, and evaluation metrics. Results reveal a convergent pipeline of detection → segmentation → quantification → decision support. On CT, 2.5D/3D candidate generation combined with boundary-aware segmentation improves performance for small nodules and ground-glass opacities. On CXR, integrating global and regional features with anatomical priors (e.g., bone suppression) mitigates projection overlap. Weak, semi-, and self-supervised learning, along with contrastive learning and knowledge distillation, enhance robustness under limited data and domain shift, while focal-type losses address class imbalance. Deployment-oriented optimizations (e.g., ONNX, TensorRT, pruning, and quantization) significantly reduce inference latency and facilitate integration with clinical systems (PACS/RIS) via structured outputs and saliency visualization. Strengths include clinically aligned pipelines and improved efficiency, whereas limitations persist in external validation, calibration, and reporting transparency. We recommend routine external “test-only” evaluation, prospective validation, standardized uncertainty reporting, and improved reproducibility practices. These steps are essential to advance CNN-based pulmonary imaging systems from experimental feasibility toward reliable clinical deployment.