# Improved U-Net: Fully Convolutional Network Model for Skin-Lesion Segmentation
## 4 Results and Discussion  
When we compared our recorded result with different U-Net methods such as the original U-Net, FCN-1 (U-Net with the PReLU and transpose convolution), and FCN-2 (U-Net with the PreLU and bilinear interpolation), we achieved acceptable metrics accuracy with the test dataset. Table 1 shows the average accuracies over different models. Via experiments, we identified that using bilinear interpolation jointly with the PreLU was an effective method for skin-lesion segmentation. The training time per epoch is around 6 min for the original U-Net, and the proposed method spends less than 5 min per epoch. Figure 10 depicts image differences from the above-given versions of U-Net. The transpose convolution method for upsampling had more false negative results in the validation set. The experiments showed that a slight underfitting was recorded in the case of using transpose convolution (see Figure 10,d). When the interpolation method was used with PreLU nonlinearity, the model detected more outliers in the validation set images. Figure 10e shows an image of high variance (overfitting). After applying the dropout technique, we overcame this problem in the interpolation method. The final visual result of the model is given in Figure 10f. We compared the validation set accuracies of each model during training (see Figure 11). After 20 training epochs, the validation accuracy of each method remained nearly the same. In Figure 11, U-Net-1 and U-Net-2 reflect FCN-1 and FCN-2, respectively. The illustration in Figure 11 helps us reach a conclusion about the learning procedure of each method. Transpose convolution-based models tend to learn faster, compared with interpolation-based methods. They almost always achieve their highest accuracy after 12 epochs. Contrarily, interpolation-based methods take much more time to achieve desired accuracies. The reason for this phenomenon is that the dropout effect causes a delay in reaching the desired accuracy. Another explanation for why bilinear interpolation-based approaches (FCN-2 and the proposed method) tend to learn more slowly than transpose convolution-based methods (U-Net and FCN-1) is that transpose \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Pixel Accuracy** & **Dice** & **IoU** \\ \hline U-Net & 91.12\% & 78.23\% & 39.26\% \\ FCN-1 & 90.47\% & 81.85\% & 40.51\% \\ FCN-2 & 91.34\% & 83.26\% & 41.84\% \\ Proposed method & 94.36\% & 88.33\% & 44.05\% \\ \hline \hline \end{tabular} Here: U-Net—original U-Net architecture; FCN-1—U-Net with the PReLU and transpose convolution; FCN-2—U-Net with the PreLU and bilinear interpolation; Proposed method: U-Net with the PreLU, bilinear interpolation and dropout.  
\end{table} 
Table 1: Test accuracy of various U-Net models. convolution uses more learnable parameters compared with interpolation methods. In the initial steps of learning, transpose convolution-based methods try to learn with higher accuracy because of their default parameters. On the other hand, interpolation-based methods do not use learnable weights and biases. Therefore, they start to learn with lower accuracy and slowly reach the desired accuracy. The graphs in Figure 12 clarify the training and validation accuracies of our technique. There is not much difference between training and validation accuracy, owing to the dropout regularization applied to the network. Figure 11: Comparing learning curves per epoch. Figure 10: Sample results of segmented skin lesion: (**a**) original image (black line is the ground truth); (**b**) ground truth; (**c**) result of U-Net; (**d**) result of FC1; (**e**) result of FCN-2; and (**f**) result of proposed model. Notes: 1. White section in the binary image is the segmented skin lesion; 2. Red lines are the ground truth of the input image. The overall dice coefficient was determined by calculating the average means of the individual dice coefficients of all images. Some DC values were relatively small because of noise in the input image.