# Design of a Lung Lesion Target Detection Algorithm Based on a Domain-Adaptive Neural Network Model
## 4 Experiment Results and Analysis
### Experiment Result and Result Analysis  
After finalizing the model construction and confirming the experimental environment, we proceeded with model training and testing. Following the 80%, 10%, and 10% split ratio specified in Algorithm 1 for training, validation, and testing, respectively, the training loss dynamics and corresponding accuracy progression during the training process were recorded. The results of these changes are illustrated in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Specifications** \\ \hline Operating system & Windows 10 \\ \hline CPU & I7-14400F \\ \hline GPUs & RTX 4080 Ti \\ \hline Language & Python 3.5.1 \\ \hline Framework & Pytorch \\ \hline \hline \end{tabular}
\end{table}
Table 1: The experiment environment information.  
Figure 3: The training process on LUNA16 datasets among different methods ([PERSON] et al., [13], [PERSON] et al., [16], [PERSON] et al., [32], [PERSON] et al., [35]).  
In Figure 3, it is evident that with continuous iterations, each model demonstrates progressively improved performance, achieving a final precision exceeding 0.8. During the loss convergence phase, the proposed CNDAD--Net exhibits superior results within approximately 15 iterations. Building upon these findings, we conducted a detailed statistical analysis of the final results, focusing on four evaluation metrics. A comparative analysis of these metrics is presented in Figure 4.  
In Figure 4, we give the specific results of the four types of evaluation metrics--mAP, recall, precision and F1-score, respectively--which are explained in detail below. For the mAP metric, mAP @ 0.5 is adopted, the overall performance is generally better than other  
Figure 4: The method comparison results on LUNA16 datasets ([PERSON] et al., [13], [PERSON] et al., [16], [PERSON] et al., [32], [PERSON] et al., [35]).  
methods under this metric, and its value reaches 0.889, which is significantly better than traditional methods such as Faster-RCNN and slightly better than the method proposed by [PERSON] et al. In addition, on the RECOLL indicator, the network proposed is 0.845, which indicates that it has a better performance in leakage detection compared to other methods, and can ensure more complete lesion detection. In addition, CNDAD--Net also has better performance in precision, which proves that it can detect relevant targets more accurately. In contrast, under the F1 metric, the framework of this paper is 0.886, which achieves a more balanced performance. In order to further test the characteristics of this paper's method, we used a ten-fold validation form for the further validation of CNDAD--Net, the results of which are shown in Figure 5.  
In Figure 5, it is observed that CNDAD--Net exhibits consistent stability across the first eight experiments for all evaluation metrics. However, in the final two validations, a slight deviation in precision is noted. This deviation is attributed to the imbalance in category distribution during these experiments, which impacts the model's performance to a certain extent. Addressing this issue will require greater emphasis on balancing categories in future datasets to ensure more robust and reliable model performance.