# Design of a Lung Lesion Target Detection Algorithm Based on a Domain-Adaptive Neural Network Model
## 2 Related Works  
### Target Detection Study  
Target detection, a cornerstone task in computer vision, involves identifying the bounding boxes of targets and determining their respective classes. While traditional methods rely on handcrafted features and operators, deep learning approaches employ deep neural networks to autonomously learn target features from data, exhibiting robust nonlinear fitting capabilities. Classical CNN-based target detection methods are generally categorized into two-stage and one-stage approaches. Notable examples of two-stage methods include Faster R-CNN [13] and R-FCN [14], while prominent one-stage methods comprise YOLO [15], SSD [16], and RetinaNet [17]. Two-stage methods are typically characterized by higher accuracy, whereas one-stage methods prioritize speed. To address specific challenges in target detection, several advancements have been proposed. [PERSON] et al. introduced Feature Pyramid Networks (FPNs), which enhance the detection of small targets by integrating features across different depths in a top-down hierarchical structure [18]. For target deformation information extraction, [PERSON] et al. developed Deformable Convolutional Networks (DCNs), incorporating deformable convolution and deformable Region of Interest (RoI) pooling mechanisms, enabling the adaptive spatial sampling of targets [19]. Additionally, [PERSON] et al. dynamically define positive and negative samples based on IoU between ground-truth and predicted bounding boxes, addressing the issue of instance allocation in detection tasks [20]. The collection of labeled datasets and the training of target detection models from scratch remain time-intensive and laborious. To alleviate these challenges, various methodologies have been explored, including semi-supervised learning, few-shot learning, and transfer learning. These approaches significantly reduce the reliance on large labeled datasets while enabling effective manipulation of rendering factors such as weather conditions and time of capture. Notably, comparisons using synthetic data on urban road scenarios have demonstrated that synthetic datasets, when optimized, can outperform real-world datasets, thereby enhancing the overall performance of target detection systems.  
It can be found from the above studies that significant advancements have been made in object detection technology, particularly with the introduction of deep learning methods, which have greatly improved detection accuracy and efficiency. Two-stage methods, such as Faster R-CNN and R-FCN, excel in detection precision, while one-stage methods, such as YOLO, SSD, and RetinaNet, offer advantages in computational speed. Additionally, various improvements, including Feature Pyramid Networks (FPNs), Deformable Convolutional Networks (DCNs), and adaptive positive-negative sample assignment strategies, have enhanced the adaptability and robustness of object detection across different scenarios. However, in the medical imaging domain, the high cost of data acquisition and annotation poses significant challenges for model training and optimization. To address these issues, researchers have explored approaches such as semi-supervised learning, self-supervised learning, few-shot learning, and transfer learning to mitigate data scarcity while improving model generalization and detection performance. Future research directions will focus on efficiently leveraging limited medical data and integrating multimodal information to further enhance detection accuracy and stability.