# Models to Identify Small Brain White Matter Hyperintensity Lesions
## 4 Results
### Comparison of Proposed Segmentation Models  
Table 10 summarizes the average DSC values of segmentation of lesions of each model experimented in this work. We can see that the UNet model (0.95) has better performance in the segmentation of the lesions, followed by Detectron2 (0.887), SAM (0.50), and YOLOv8 (0.264). Table 11 summarizes the results of some recent works concerning the segmentation of WMHs compared to the proposed models in this project. We can note that our proposed UNet model stands out for its DSC value (0.95). \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model/Metric** & **UNet** & **SAM (Vit Large)** & **YOLOv8** & **Detectron2** \\ \hline DSC & 0.95 & 0.50 & 0.264 & 0.887 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: DSC values of segmentation lesions models proposed. Brief Comparison Results Between Expert Criteria and YOLO and Detectron2 Models for Classification of Ischemia and Demyelination Lesions  
This section compares the detection and classification of ischemia and demyelination lesions using the approaches of YOLOv8 and Detectron2 models against the metrics of two expert radiologists, each with more than ten years of experience. Table 12 shows the visual and statistical comparison of test images/lesions (21) and their detection classification using the Detectron2 and YOLOv8 models against the radiologist expert. As we can see in Figures 28-30, the best results for classifying and detecting the ischemia and demyelination lesions are obtained by the Detectron2 model. In Table 12, the values of the Kappa (0.809) and the corresponding \(p\)-value (0.0534) indicate a substantial agreement between Expert criteria and Detectron2 model with marginal significance, while for YOLOv8, it is seen that the Kappa value (0.0350) and the \(p\)-value (0.0001) show a poor agreement between the expert criteria and YOLOv8 model, with a significant difference. In conclusion, as we can see in Figure 26, the Detectron2 model has better performance than YOLOv8; their metrics do not separate the Expert criteria but are relatively near these values. In this sense, this model could be implemented in clinical environments in a CAD system or as a second opinion in the diagnosis. Figure 31 shows the Receiver Operating Characteristic (ROC) curve that compares the classifiers' performance. In this case, we can see that the value of Area Under the Curve (AUC) for Detectron2 (AUC = 0.929) is better than YOLOv8 (AUC = 0.524), which means that the Detectron2 model has a good performance, close to the experts (AUC = 0.976) concerning the identification and classification of the lesions. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Expert**} & \multirow{2}{*}{**Detectron2**} & \multirow{2}{*}{**YOLOv8**} & \multirow{2}{*}{**Comparison**} & \multirow{2}{*}{**Kappa**} & \multirow{2}{*}{**\(p\) < 0.05**} \\  & & & & & **(DeLong’s Test)** \\ \hline Accuracy & 0.976 & 0.928 & 0.523 & & Expert vs. \\ Precision & 0.954 & 0.909 & 0.600 & Detectron2 & 0.809 & 0.0534 \\ \hline Recall & 0.997 & 0.952 & 0.142 & & Expert vs. \\ F1 score & 0.976 & 0.930 & 0.230 & YOLOv8 & 0.035 & 0.0001 \\ \hline Sensitivity & 0.995 & 0.952 & 0.142 & & Detectron2 vs. \\ Specificity & 0.952 & 0.904 & 0.904 & YOLOv8 & 0.035 & 0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Classification metrics with a set of test images/lesions for comparison between Expert, Detectron2, and YOLOv8 models.