## 3 Results  
The results in this section are divided in three parts: (A) clinical feature comparison of single-frame analysis and multi-frame performance, (B) performance of the three DCNNs and its ensemble, and (C) comparison of our work with the literature. A. Clinical feature comparison. In this section we report the results obtained in the multi-frame analysis provided per our demand to the authors of [PERSON] et al. [15]. We compared the multi-frame classification results with the single-frame work. The multi-frame analysis for the LH feature achieved an average area under the receiver operating characteristic curve (AUC) of \(0.90\pm 0.07\) compared to the single-frame analysis, which achieved an AUC of \(0.86\pm 0.02\). In the same manner, the GS feature achieved an average AUC of \(0.83\pm 0.01\) for the multi-frame analysis, in comparison with an average AUC of \(0.84\pm 0.02\) for the single-frame methodology. Overall, we observe that the GS feature does not achieve better performance than the single-frame technique. Alternatively, we saw an increased performance on the LH feature when the multi-frame analysis is applied (AUC 0.90 vs. 0.86). Figure 3: Architecture of the deep convolutional neural network (DCNN) used in this study, which is based on VGG16. B. Performance of the three DCNNs and its ensemble. In order to validate the ensemble of DCNNs, we have evaluated our approach using the unseen test dataset. The posterior probabilities were computed for each VLE frame using the three trained DCNNs. For each ROI, a total of 51 possible predictions were obtained for each DCNN. The final probability of an ROI belonging to NDEE or HGD was computed by averaging the total number of probabilities in each DCNN, reported as the multi-frame probability. In Equation (1), the multi-frame probability can be explained as the decision of an ROI belonging to certain class A, computed by averaging the total probabilities of \(M\) frames for a number of \(N\) networks, where in our case \(M=51\) and \(N=3\). \[P(A)_{ROI}=\frac{1}{N\times M}\sum_{i=0}^{N-1}\sum_{j=0}^{M-1}P_{ij}(A). \tag{1}\]  
Given a multi-frame probability, we computed accuracy metrics for both the training dataset and the test dataset. Additionally we computed the sensitivity, defined as the rate of HGD ROIs that are correctly identified as such (true positives), and specificity, defined as the proportion of NDEE ROIs that are correctly identified as such (true negatives). The receiver operating characteristic curve (ROC) was computed as well for both datasets for each trained DCNN. In Table 2, we present the results of the ensemble of DCNNs. To showcase the efficacy of our method, we report the classification results using the single-frame and multi-frame analysis for each of the training dataset and the test dataset. All metrics are reported with confidence intervals (CIs) at 95%. All presented values are reported at the optimal threshold setting, which was calculated from the training dataset. We observe that similar to the results presented in Section 3, we obtain an increased performance when accounting the multi-frame probability for both the training dataset (AUC, 0.98 vs. 0.95) and the test dataset (AUC, 0.96 vs. 0.90). Figure 4 portrays the computed ROC for the test dataset and the associated confusion matrix. The ROC curve was computed for each trained DCNN and the total ensemble. It shows that the combination of the three DCNNs improves the performance of our model (AUC, 0.96 vs. 0.92-0.96) (Figure 4a). C. Comparison of our work with the literature. Previous work done in VLE images [14; 15] focused on extracting features to detect neoplasia in BE patients. To compare with the literature, we have extended the work of [PERSON] et al. [15] for multi-frame analysis. In Table 3, we compare our results with recent work on VLE, as well as highlight the nature of each of the studies.