# Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps
## 3 Results and Discussion
### Colonoscopy Still Frame Analysis  
Our model was trained with the CVC-ClinicDB database with 612 still frame polyp images. In the training phase, 536 images were used for training the model, and the remaining images were utilized as a validation dataset for tuning the model. The train dataset was split into mini-batches; each mini-batch had approximately 32 images. Data augmentation was randomly applied on 50% of the images in each mini-batch, and the remaining images were left undisturbed. The trained model weights were saved and loaded for predictions of testing datasets (the CVC-ColonDB, CVC-PolypHD, and ETIS-Larib datasets). The ResNet model (ResNet-50 and ResNet-101) was trained individually with the CVC-ClinicDB polyp database and initialized with pre-trained weights from COCO, ImageNet, and Flickr's balloon data. Table 2 illustrates the results from testing the model on the CVC-ColonDB database. The results from the CVC-PolypHD images are shown in Table 3. Table 4 gives the detection information from the ETIS-Larib database. There was no uniform performance from these datasets, as these data were extracted from different patients. On detailed observation, it could be found that the features extracted from ResNet-101 with Flickr's balloon pre-trained weights tended to perform better with an optimal number of false positives and false negatives. This is understandable, because the ResNet-101 model has relatively deeper architecture that can extract more feature information from the image data, and the balloon data closely resembles the shape of the polyps. It was observed that there was no problem of overfitting with the deeper model, thanks to the image augmentation. This infers that the network is better at generalizing the polyp data. This can be observed from the learning curves (Figure 4) as well as from the prediction results on test datasets. \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 & 271 & 39 & 28 & 87.42 & 90.64 & 89.00 & 89.97 \\  & ImageNet & 280 & 45 & 19 & 86.15 & 93.65 & 89.74 & 92.04 \\  & Balloon & 274 & 31 & 25 & 89.94 & 91.64 & 90.73 & 91.27 \\ \hline \multirow{3}{*}{Resnet-50} & COCO & 274 & 95 & 25 & 74.25 & 91.64 & 82.04 & 87.54 \\  & ImageNet & 287 & 77 & 12 & 78.85 & 96.00 & 86.58 & 92.00 \\ \cline{1-1}  & Balloon & 276 & 104 & 23 & 72.63 & 92.31 & 81.30 & 87.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results from testing on CVC-ColonDB polyp data. COCO: Common Objects in Context, TP: true positives, FP: false positives, FN: false negatives. \begin{table}
\begin{tabular}{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 & 47 & 8 & 17 & 85.45 & 73.44 & 79.00 & 75.56 \\  & ImageNet & 49 & 8 & 15 & 85.96 & 76.56 & 81.00 & 78.27 \\  & Balloon & 50 & 10 & 14 & 83.33 & 78.12 & 80.65 & 79.11 \\ \hline \multirow{3}{*}{Resnet-50} & COCO & 52 & 12 & 12 & 81.25 & 81.25 & 81.25 & 81.25 \\  & ImageNet & 50 & 9 & 14 & 84.75 & 78.12 & 81.30 & 79.37 \\ \cline{1-1}  & Balloon & 48 & 8 & 16 & 85.71 & 75.00 & 80.00 & 76.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results from testing on CVC-PolypHD polyp data. The still images from the ETIS-Larib database had a good comparison framework in the MICCAI sub-challenge. Table 5 compares the results from various submissions in the challenge, with the proposed approach having the best optimal results. The tabulated results are based on the predictions on the ETIS-Larib polyp database. The top results are observed from the teams (CUMED, OUS, and UNS-UCLAN) that used a CNN-based fully convolutional network (FCN) for end-to-end learning. CUMED [43] employs multi-level feature representation with FCN for pixel-wise classification.