# Design of a Lung Lesion Target Detection Algorithm Based on a Domain-Adaptive Neural Network Model
## 6 Conclusions  
In this study, we deeply explored the challenge of lung lesion target detection and proposed the CNDAD--Net, which integrates domain adaptation techniques with multi-scale feature fusion. This framework addresses the limitations of traditional detection methods in handling cross-domain data distribution disparities and complex lesion morphologies.  
First, the input data are preprocessed by a classical encoding-decoding structure to provide a robust foundation for subsequent feature learning. Utilizing the domain discriminator in GAN aligns feature distributions between the source and target domains, and significantly improves the generalization of the model in cross-domain scenarios. Finally, CFB is integrated into the model effectively to fuse 2D, 3D, and domain-adapted features. This module also enhances the capability of the model to sense and detect regions of lesion areas for good performance. Experimental tests that were carried out on the LUNA16 dataset result in an exceptional performance from CNDAD--Net with accuracy near 0.9 for the main medical metrics such as mAP and recall of the target detection task. These results form a very good basis for its usage in clinical applications in the future.  
Despite the strong performance of our model in lung nodule detection, several limitations remain. First, deep learning-based detection methods have high computational costs, particularly 3D CNNs and domain adaptation techniques, which require substantial GPU resources. This poses challenges for deployment in low-resource medical settings or real-time applications. Second, the model's generalization ability in real-world clinical applications is still a concern. Domain shifts caused by differences in imaging protocols, scanner types, and patient demographics can lead to performance degradation when applied in diverse hospital environments. To address these limitations, future research will focus on optimizing computational efficiency through model compression, lightweight architectures, and cloud-based inference solutions, making real-time applications more feasible. Additionally, we plan to extend the model's capabilities by integrating multimodal medical imaging data, such as X-ray and MRI scans, to enhance its adaptability across different imaging modalities. Further exploration of advanced feature extraction techniques, including Transformer-based architectures and more efficient convolutional networks, is expected to improve detection performance. Lastly, improving model robustness in clinical settings through self-supervised learning, continual learning, and federated learning will be essential in mitigating domain shift issues and enhancing generalization across medical institutions. These improvements will contribute to the development of a more scalable, efficient, and clinically applicable AI-driven lung nodule detection system.  
Conceptualization, X.L. and W.L.; methodology, X.L.; software, W.L.; validation, X.L., W.L. and A.W.; formal analysis, X.L.; investigation, W.L.; resources, X.L.; data curation, A.W.; writing--original draft preparation, A.W.; writing--review and editing, A.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We thank the anonymous reviewers whose comments and suggestions helped to improve the manuscript.  
The authors declare no conflicts of interest.