# Design of a Lung Lesion Target Detection Algorithm Based on a Domain-Adaptive Neural Network Model
## 4 Experiment Results and Analysis  
### Dataset and Experiment Setup  
LUNA16 (LUng Nodule Analysis 2016) is a benchmark dataset specifically designed for lung nodule detection tasks, derived from the LIDC-IDRI (The Lung Image Database Consortium Image Collection) dataset [32; 33]. It is widely used in the field of pulmonary CT image analysis to evaluate the performance of various lung nodule detection algorithms. Each nodule in the dataset has been annotated by multiple radiologists, following a consistency scoring system (ranging from 1 to 5) that reflects the level of suspicion assigned by the radiologists. Only nodules with diameters between 3 mm and 30 mm are included in the dataset [34].  
The dataset contains a significant proportion of non-nodular regions, or background areas, posing substantial challenges for detection algorithms. This necessitates meticulous attention to data annotations. In this study, we aim to enhance model performance by leveraging domain adaptation techniques within a transfer learning framework to expand the detection range and improve robustness.  
For evaluation metrics, pulmonary nodule detection commonly employs precision, recall, F1-score, and mean average precision (mAP), which are calculated as follows:  
\[AP=\int\limits_{0}^{1}P(r)dr \tag{7}\]  
\[mAP=\frac{1}{n}\sum\limits_{i=1}^{n}AP_{i} \tag{8}\]  
The AP is determined by plotting the precision-recall (P-R) curve. The area under the P-R curve represents the AP. The mAP is calculated by averaging the AP values across all categories. Since this study focuses exclusively on the single-category detection of lung nodules, \(n=1\)\(n=1\), simplifying the calculation to a single AP value. Following the construction of the model, further analysis was conducted. For method comparison, we selected the mainstream YOLOv7 model and compared its performance with the improved YOLO-KMeans model proposed by [PERSON] et al. [32], the DETR model introduced by [PERSON] et al. [35], as well as the foundational models Faster R-CNN [13] and SSD [16]. Among them, [PERSON] et al.'s method can be regarded as a benchmark method which employs the most classical YOLO. In addition to comparative analysis, ablation experiments were performed to evaluate the contributions of different components within the proposed framework. Specifically, we examined the performance of the CND--Net without domain adaptation, as well as the C2D--Net and C3D--Net models, which utilize single 2D and 3D data features, respectively, without employing the proposed multi-scale and multidimensional data fusion techniques. These experiments provided critical insights into the impact of domain adaptation and feature fusion on the overall model performance.  
And image data requires a large computational overhead, so the experimental environment built is as Table 1:  
For model training, we adopted a deep learning-based detection network that leverages a 3D convolutional neural network (3D-CNN) backbone, pre-trained on medical image datasets to enhance feature extraction. Given the domain shift challenge in medical imaging, we incorporated transfer learning with domain adaptation to improve detection performance across different CT scan distributions. Specifically, an adversarial domain adaptation strategy was implemented to align feature distributions between source and target domains. The model was trained using the Adam optimizer, with an initial learning rate of 1e\(-\)4, batch size of 16, and training of 50 epochs. To mitigate class imbalance between nodules and non-nodule regions, we employed focal loss and hard negative mining techniques.