## 4 Results
### Segmentation Results  
Colorectal cancer and its predecessor disease segmentation results can be evaluated using different measures. It is highly dependent on the rate of detection as well as on the fraction between complete pixels and diseased pixels. To check the effectiveness of segmentation using U-Net with ResNet-34 as a backbone model, we performed a set of experiments on the Kvasir-Seg dataset. Performance measures used to check the efficiency of segmentation are dice, mIOU, precision, and recall.  
Dice, which is also known as the overlap measure, is the most frequently used measure for evaluating and testing the effectiveness of medical image segmentation [44]. This overlap region between the predicted segmented image and the ground truth is doubled, and the result is divided by the total number of pixels in both images. mIOU, known as the mean intersection over union, is usually used to check for medical segmentation. IOU is calculated as the anticipated segmentation overlap over the ground truth divided by the total number of pixels. Mean IOU is calculated by taking the IOU of each label and averaging them. A precision measure is defined as the quality of being accurate. It measures the quality of our predictions. Recall is a measure used to calculate the positive points in the ground truth that are predicted positively by a model. Mathematical equations of these performance measures are provided as Equations (8)-(11).  
\[Dice=\frac{2*\text{ M}}{2*\text{M}+N+\text{O}} \tag{8}\]  
\[mIOU=\frac{M}{M+N+\text{O}} \tag{9}\]  
\[Precision=\frac{\text{M}}{\text{M}+N} \tag{10}\]  
\[Recall=\frac{\text{M}}{M+\text{O}} \tag{11}\]  
where M is defined as true-positive, N is defined as false-positive, O is defined as false-negative, and P is defined as true-negative.  
For the segmentation results, the K-fold cross-validation technique was applied with K fixed to 10, as it is evident from research that when K is 10, the model performs better [40]. After we applied U-Net with ResNet-34 as a backbone model on our dataset, the model achieved a 0.9030 mIOU score, 0.8208 dice score, 0.9435 precision, and 0.8597 recall score. Table 1 compares the quantitative findings based on the Kvasir-Seg dataset using several segmentation methods.  
Qualitative results of segmentation and localization of polyps based on the Kvasir-Seg dataset are shown in Figure 7. By looking at the ground truth, it can be noticed that the segmentation results generated by UNet with the ResNet-34 model as a background are up to the mark. Furthermore, the results show that the model detected the large diseased area and produced high-quality masks at similar locality but with a slightly different shape. This same segmentation model is applied to all other classes, like ulcer, polyp, ulcerative colitis, and esophagitis, for drawing contours around the diseased area and passing these images for classification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sr. No.** & **Method** & **Dice** & **mIOU** & **Precision** & **Recall** \\ \hline
1. & ResUNet [45] & 0.5144 & 0.4364 & 0.7292 & 0.5041 \\
2. & ColonSegNet [46] & 0.7980 & 0.6980 & 0.8432 & 0.8193 \\
3. & HarDNet-MSEG [47] & 0.8102 & 0.7459 & 0.8652 & 0.8485 \\
4. & PraNet [48] & 0.8142 & 0.8796 & 0.9126 & 0.8453 \\
5. & UNet with ResNet-34 & **0.8208** & **0.9030** & **0.9435** & **0.8597** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative findings based on the Kvasir-Seg dataset.  
Figure 7: Qualitative findings based on the Kvasir-SEG dataset after applying U-Net with ResNet-34 as the backbone.