# Models to Identify Small Brain White Matter Hyperintensity Lesions
## 5 Discussion
This could be due to the quantity of the data used because, in our case, we used only 220 images for classification, while, for example, [PERSON] et al. [111] proposed a methodology for the detection of stroke lesions using the Faster R-CNN, YOLOv3, and SSD (Single Shote Detector) with a private dataset of 5668 brain MRI images, and reported a lesion detection with the best precision of 89.77%; however, they only detected big lesions without classification. However, in this project, we use the YOLOv8 to test, evaluate, and compare the classification and detection of the lesions. In this sense, we can see in the confusion matrix (see Figure 19) that it is difficult for the model to distinguish between the two types of lesions due to similarities in their appearance on MRI scans and the size of the lesions. According to the literature [59], YOLO is not good at detecting and classifying small lesions. For that reason, another approach is proposed, where YOLOv8 only detects lesions if WMH lesions are without classification, and the model reports a DSC value of 0.264 (see Figure 16 and Table 10). The YOLO model has better results but still needs improvement. Some studies [67; 129] dealt with detecting brain tumors where the lesions are bigger, such as the detection of gliomas, meningioma, and pituitary brain tumors using YOLOv7 and YOLOv5, reporting results of 99.5% and 90% accuracy, respectively. Additionally, in these works, the datasets used are larger than those used in this project. Therefore, this is a good point for initiating more experiments in future studies with further refinement, additional training data, or other architectures. In this sense, in agreement with works [62; 130; 131], it is proposed that YOLO can improve detection results when combined with other models, e.g., SAM. Regarding the Detectron2 model, we can see from the results that this proposed model performs well in detecting (0.94) and classifying lesions (0.98), including small ones and with irregular morphology (see Figure 24 and Table 9). One advantage over the other is that Detectron2 also allows the delineation of the shapes of the lesions; in this sense, the results acquired have more confidence. These values agree with others reported in the literature, e.g., in [116], where the goal was detecting the WMH lesions using MaskRCNN from Detectron2, which reports a value of 0.93 for the stroke dataset and 0.83 for the WMH dataset (see Table 13). In our work, our result for segmentation has a value of DSC of 0.887, and for detecting the lesions, WMH is 0.94 (see Tables 8 and 13). Also, it is important to mention that the YOLOv8 and Detectron2 models were designed with the possibility of selecting and adjusting the threshold of detection for clinical detection. That means that depending on our task, we can adjust the threshold to get segmented lesions and ensure customized sensitivity based on clinical requirements. In this project, the YOLOv8 threshold was set to a value of 0.2 to get better detection results. For Detectron2, the threshold values are 0.5 and 0.8; for the value of 0.8 (see Figure 29), it is more confident with the acquisition values due to the medical data's sensitivity. The opportunity to select a threshold of detection also increases the transparency of physicians in order to know what the system is doing to classify lesions. Therefore, based on the results of the comparison against the expert's criteria (see Table 12 and Figures 30 and 31), we can say that the better approach for detecting and differentiating the brain lesions of ischemia and demyelination is the Detectron2 model. The values of the Kappa (0.809) and the corresponding \(p\)-value (0.0534) (see Table 12) 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) shows a poor agreement between the expert criteria and YOLOv8 model, with a significant difference.