## 5 Discussion and Conclusions  
### Discussion  
This study incorporated bagging ensemble and down-sampling techniques to overcome the hurdles from an imbalanced dataset and achieve better performance. The down-sampling techniques and asymmetric data augmentation used are known to mitigate the imbalanced dataset's impact on the experimental results. As the number of benign cases was considerably less than malignant cases, four data augmentation methods were implemented for benign cases, and two were used for malignant cases. According to the experimental results, all ensemble results were better than the baseline result. <in addition, we explored various same and different model combinations and integrated two or three models while considering the amount of data available for each model. We employed five-fold cross-validation to evaluate performance, as this assessment determines a model's generalization ability, revealing that each fold's results fluctuated. This variation in results across folds can be influenced by training and validation data split compositions, the dataset's inherent variability, and the models' sensitivity to different data subsets. All bagging ensemble experiment result are shown in Tables 5-7. Three fusion methods were used in the bagging experiment. Table 5 displays the bagging experimental results with the majority voting fusion method, and Table 4 presents the baseline experimental results. Even with data augmentation to alleviate the imbalanced dataset's impact, the ResNet-34 backbone did not perform well. One explanation may be that the number of benign cases was insufficient; both baseline and bagging ensemble results evidenced that the PPV was better than the NPV. These findings indicate that trained CNN models can more accurately differentiate malignant cases than benign ones. Notably, the number of malignant cases was much larger than the benign cases in this study; thus, the trained CNN models acquired more knowledge regarding malignant cases than benign ones. Additional benign case data should be collected for future investigations. Table 6 shows the bagging results with the output average fusion method. The output average fusion method yielded the best results within the ensemble experimental results of three fusion methods and aggregating the same CNN models. A ResNet-18 backbone and two-CNN model combination revealed good results: 0.70 accuracy, 0.63 F1-Score, 0.70 AUC, 0.87 PPV, 0.67 NPV, 0.56 sensitivity, and 0.84 specificity. Table 7 displays the bagging results with the performance weighting fusion method. When aggregating two or three same CNN models, the ResNet-18, ResNet-34, DenseNet-121, and MobileNet-V2 accuracies were remarkably similar. Among all experimental results, a ResNet-18 backbone and the performance weighting fusion method yield the best specificity value, as shown in Table 7.  
Based on the comparison results between the best baseline and bagging ensemble results (Table 8), the benign and malignant PPL classification improved. This study conducted five-fold cross-validation to evaluate the proposed CAD system's performance further. The dataset division results are presented in Table 3. Although the CNN models performed well in some folds, this was not the case for others. This finding reveals that the data distribution of the five-fold dataset was not the same, as using the same CNN model backbone indicated significant differences between results. Another concern involves using pre-trained CNN models for feature extraction. The training results were considerably better than the validation and testing results, potentially due to the CNN models' depth. In addition, compared with ImageNet images, EBUS is more practical. Deep CNN models are not necessary for feature extraction; therefore, we intend to construct shallow convolutional layers for feature extraction in the future. In addition to using the fully connected layers, we will explore SVM, decision trees, and random forests as classifiers.