# Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps
## 3 Results and Discussion
### WCE Video Analysis  
A similar approach is employed for training and testing WCE videos for colonoscopy images, as mentioned earlier. Data augmentation is also applied on the training image dataset for the model to better generalize on different kinds of polyps. The results as shown in Figure 5 indicate the polyp detection and segmentation in the WCE videos. Table 7 illustrates the results from the WCE data. The tabulated values clearly indicate ResNet-101 as the best feature extractor, and the model trained on Flickr's balloon pre-trained weights is clearly a winner. There is no standard framework to compare the results with other techniques for the WCE polyp detection. It can be observed from the segmentation results that bubbles are detected as polyps; avoiding this misclassification is challenging due to limited frames with bubbles available to make the model learn that bubbles are not polyps. Some of the polyps look similar to the mucosa membrane, which makes it difficult for the model to segment on such polyps. Table 8 provides an evaluation based on the segmentation results of the WCE polyp frames with and without data augmentation. Applying augmentation has helped the model learn more about the input data. A similar technique was employed for augmentation for colonoscopy images. The train dataset was split into mini-batches such that each mini-batch had 32 images and 50% of the randomly selected images in each mini-batch were augmented with different methods. The tabulated results show a higher detection of polyp images with data augmentation. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model Backbone** & **Pre-Trained Weights** & **TP** & **FP** & **FN** & **P** & **R** & **F1** & **F2** \\ \hline \multirow{3}{*}{Resnet-101} & COCO & 63 & 4 & 4 & 94.03 & 94.03 & 94.03 & 94.03 \\  & ImageNet & 64 & 14 & 3 & 82.05 & 95.52 & 88.28 & 92.49 \\  & Balloon & 64 & 1 & 3 & 98.46 & 95.52 & 96.67 & 96.10 \\ \hline \multirow{3}{*}{Resnet-50} & COCO & 64 & 4 & 3 & 94.12 & 95.52 & 94.81 & 95.24 \\  & ImageNet & 58 & 5 & 9 & 92.06 & 86.57 & 89.23 & 87.61 \\ \cline{1-1}  & Balloon & 63 & 7 & 4 & 90.00 & 94.03 & 91.97 & 93.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results from testing on the Mayo Clinic wireless capsule endoscopy (WCE) polyp data. Figure 5: Polyp segmentation in colonoscopy images (ETIS-Larib). Blue overlay is the predicted segmentation mask. The contour of the ground truth masks is shown in red. Detection points are masked in green. Figure 6 displays the detection and segmentation results on the WCE polyp test data based on the proposed approach with ResNet-101 and Flickr's balloon data. Although the model performs better in localizing polyps, there is still the possibility of improving the model. The first limitation for designing an automated polyp detection system is the limited availability of the data. Currently, additional data is generated online during training through data augmentation. Increasing the database with more polyp images will definitely improve the results and also help the model better generalize in its prediction of new polyp images. Recent studies on deep learning frameworks show improved classification and segmentation results on natural images with deeper architectures. However, there is a trade-off regarding the speed of training and testing the models. Deeper CNNs have a greater number of parameters that require more storage and GPU capability for better performance. In the future, the feature extractor part of the model can be replaced with deeper models to extract much better and more detailed feature maps. The second stage for generating a mask can be experimented with different combinations of architectures. The loss function plays a major role in backpropagation. If the loss function can be designed to better understand and differentiate the predicted output mask against the ground truth, there can be a gain in the performance. False positives and false negatives pose challenges in polyp detection performance.