# Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps
## 3 Results and Discussion
### Colonoscopy Still Frame Analysis
OUS uses the AlexNet model [44] as the CNN with CaffeNet for the binary classification of the image patches to detect the presence of polyps. UNS-UCLAN trained three CNNs at three different scales for feature extraction and used a multi-layer perceptron (MLP) network for classification. It can be clearly seen that the proposed model outperforms the best model (CUMED) from the MICCAI sub-challenge. The proposed approach has more FP, which limited the precision value to 72.93. Most of these FP are due to the reflections of light on the mucosa and some polyp-like structures that are not actually polyps. FN are due to the misdetections of some challenging polyp shapes; there were very few of these in the training dataset. The proposed model competes with the CUMED model [43] and appears to outperform it in the polyp detection task. Table 6 represents the evaluation results with and without data augmentation on the ETIS-Larib database. There is a huge performance improvement when the model is trained with differently oriented and transformed images. For every epoch, the model learns and tunes with a mixture of original data and augmented data. The results show a great margin of improvement by properly choosing the augmentation methodologies. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **TP** & **FP** & **FN** & **P** & **R** & **F1** & **F2** \\ \hline W/o data augmentation 1 & 147 & 73 & 61 & 66.82 & 70.67 & 68.69 & 69.87 \\ With data augmentation 1 & 167 & 62 & 41 & 72.93 & 80.29 & 76.43 & 78.70 \\ \hline \hline \end{tabular} 1 Model trained with ResNet-101 as the backbone with pre-trained weights from Flickr’s balloon data.  
\end{table} 
Table 6: Results with and without data augmentation on ETIS-Larib polyp data. \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 & 161 & 58 & 47 & 73.52 & 77.40 & 75.41 & 76.59 \\  & ImageNet & 160 & 63 & 48 & 71.75 & 76.92 & 74.25 & 75.83 \\  & Balloon & 167 & 62 & 41 & 72.93 & 80.29 & 76.43 & 78.70 \\ \hline \multirow{3}{*}{Resnet-50} & COCO & 146 & 116 & 62 & 55.73 & 70.19 & 62.13 & 66.73 \\  & ImageNet & 160 & 93 & 48 & 63.24 & 76.92 & 69.41 & 73.73 \\ \cline{1-1}  & Balloon & 141 & 86 & 67 & 62.11 & 67.79 & 64.83 & 66.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results from testing on ETIS-Larib polyp data. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **TP** & **FP** & **FN** & **P** & **R** & **F1** & **F2** \\ \hline Proposed approach 1 & 167 & 62 & 41 & 72.93 & 80.29 & 76.43 & 78.70 \\ CUMED & 144 & 55 & 64 & 72.3 & 69.2 & 70.7 & 69.8 \\ OUS & 131 & 57 & 77 & 69.7 & 63.0 & 66.1 & 64.2 \\ UNS-UCLAN & 110 & 226 & 98 & 32.73 & 52.8 & 40.4 & 47.1 \\ PLS & 119 & 630 & 89 & 15.8 & 57.2 & 24.9 & 37.6 \\ ETIS-LARIB & 103 & 1373 & 105 & 6.9 & 49.5 & 12.2 & 22.3 \\ SNU & 20 & 176 & 188 & 10.2 & 9.6 & 9.9 & 9.7 \\ \hline \hline \end{tabular} 1 Model trained with ResNet-101 as the backbone with pre-trained weights from Flickr’s balloon data.  
\end{table} 
Table 5: Comparison of proposed approach with various state-of-the-art polyp localization methods on the ETIS-Larib database. Figure 5 illustrates the ETIS-Larib polyp detection and segmentation results by the proposed model with the ResNet-101 model and pre-trained weights from Flickr's balloon dataset.