# Ultrasound Intima-Media Complex (IMC) Segmentation Using Deep Learning Models
## 4 Experimental Results
### Evaluation  
To evaluate the ultrasound IMC segmentation using the deep learning methods on the CUBS dataset, a set of metrics as mentioned above are used. These metrics are predominantly used for image segmentation in computer vision tasks. Moreover, we compare the frames per second (FPS) for each model on the same dataset. In this section, we present the obtained results from the dataset using the proposed method for ultrasound IMC segmentation. The results are reported in the tables and figures to show the performed techniques using the different architectures. We first investigated the effect of replacing CNN layers with self-ONN layers in the DeepCrack model. The level of linearity was controlled using the parameter \(q=3\), 5, 7, 9 or 11. Figure 3(a) shows that the best performing model uses \(q=3\), then the accuracy of the model starts to drop as we enlarge the level of non-linearity. Compared with the CNN \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Learning Rate** & **Optimizer** & **Epochs** & **Training Parameters** \\ \hline DeepCrack & 0.0001 & [PERSON] & 100 & 14.720 M \\ \hline DeepCrack\_Self\_ONN & 0.0001 & [PERSON] & 100 & 44.144 M \\ \hline PidiNet & 0.005 & [PERSON] & 70 & 1.150 MB \\ \hline Transformer & 0.00001 & [PERSON] & 70 & 104.609 M \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Training hyperparameters and parameters for each model. version of the model, Figure 4b shows that the best precision and recall accuracies at \(q\) are set to 3 and 5. The performance of all the deep learning models on the CUBS dataset is shown in Table 2. From the table, we can observe that both the transformer- and pixel difference-based models act similarly in all the performance measures with a slight increase for PiDiNet in the F-measure, Dice and Jaccard index. Both the transformer- and pixel difference-based models achieved better performances with exceptional margins compared to the CNN- and self-ONN-based models. From Table 2, we can also see that the post-processing operations improved the performance metrics of all the methods, including DeepCrack, DeepCrack_Self_ONN, PiDiNet and the transformer-based models. The models achieved an improvement of about 20, 14, 19 and 20% for the DeepCrack, DeepCrack_Self_ONN, PiDiNet and transformer-based models, respectively, on the precision metric, while the transformer-based + post-processing model demonstrated the best metrics followed by PiDiNet + Post-processing with an average difference of 1% and 10% and less than 1% for dice, recall and precision, respectively. In addition to the qualitative results, we present the qualitative results in Figure 5 that show the visual outputs from the segmentation results. From Figure 5, we can see that all the proposed methods demonstrated segmentation with good performance with a difference in terms of thickness. It is worth mentioning that image segmentation algorithms typically rely on edge detection and thresholding techniques to separate regions of interest from the background. However, these techniques can be affected by image noise, leading to the detection of false edges and the inclusion of noise as part of the segmented object. Additionally, image segmentation algorithms may also introduce a level of smoothing or blurring to the image, which can further contribute to the fattening effect. This smoothing operation can cause the