# Root Dilaceration Using Deep Learning:
## 3 Results  
The performance evaluation process of the models can be analyzed in two different parts. Detection performance is evaluated by mean average precision (mAP) and classification is assessed by accuracy, precision, recall, and F1 score that were calculated by the Intersection over Union of 0.5 for detection. Additionally, COCO metrics in addition to precision-recall curve, AUC and loss graphs were also provided. Table 3 presents root dilaceration detection results using Faster R-CNN, R-CNN, SSD, YOLOv3, RetinaNet, anchor-free models and models with alternative frameworks applied to our dataset. The detection performance of the models was analyzed by mAP that varied between 0.68 and 0.92. Except SSD, all other models successfully detected root dilacerations with a success rate higher than 0.83. The best two models were FreeAnchor Resnet50 and Cascade RCNN Resnet101 with 0.92 and 0.9. Accuracy varied between 0.48 and 0.72, and the best accuracy was provided by the Cascade RCNN Resnet101 and RetinaNet Resnet50 models with 0.72. Precision varied between 0.66 and 0.91. Faster RCNN Resnet101, Libra RCNN Resnext101 and FreeAnchor Resnet50 showed the highest precision with 0.91. Recall varied between 0.59 and 0.87. Faster RCNN with weight standardization (WS) and batch-channel normalization (BCN) and RegNetx provided the highest recall with 0.87 and 0.86. F1 score varied between 0.65 and 0.83. Cascade RCNN Resnet101 and RetinaNet Resnet50 were \begin{table}
\begin{tabular}{c c c c c c} \hline
**Detector—Backbone** & **mAP** & **A** & **P** & **R** & **F1 Score** & **T-Time** \\ \hline Cascade RCNN Resnet101 & 0.9 & 0.72 & 0.83 & 0.84 & 0.83 & 5 h-28 m \\ \hline Faster RCNN Resnet101 & 0.84 & 0.62 & 0.91 & 0.66 & 0.77 & 4 h-11 m \\ \hline RetinaNet Resnet50 & 0.89 & 0.72 & 0.9 & 0.78 & 0.83 & 2 h-54 m \\ \hline Yolov3 DarkNet53 & 0.87 & 0.63 & 0.72 & 0.82 & 0.77 & 2 h-38 m \\ \hline SSD & 0.68 & 0.48 & 0.66 & 0.65 & 0.65 & 3 h-31 m \\ \hline RegNetx & 0.85 & 0.69 & 0.77 & 0.86 & 0.82 & 4 h-15 m \\ \hline Libra RCNN Resnext101 & 0.87 & 0.67 & 0.91 & 0.72 & 0.80 & 16 h-02 m \\ \hline Deformable CNs & 0.83 & 0.62 & 0.76 & 0.77 & 0.76 & 8 h-52 m \\ \hline DetectoRS Resnet50 & 0.83 & 0.64 & 0.83 & 0.74 & 0.78 & 13 h-55 m \\ \hline Dynamic RCNN Resnet50 & 0.87 & 0.7 & 0.83 & 0.81 & 0.82 & 3 h-13 m \\ \hline NAS FPN & 0.85 & 0.69 & 0.88 & 0.76 & 0.81 & 2 h-32 m \\ \hline Grid RCNN Resnext101 & 0.83 & 0.64 & 0.77 & 0.79 & 0.78 & 12 h-33 m \\ \hline HTC RCNN Resnext101 & 0.89 & 0.67 & 0.79 & 0.81 & 0.80 & 11 h-00 m \\ \hline Faster RCNN with WS BCN & 0.85 & 0.64 & 0.71 & 0.87 & 0.78 & 3 h-47 m \\ \hline FreeAnchor Resnet50 & 0.92 & 0.56 & 0.91 & 0.59 & 0.72 & 3 h-7 m \\ \hline FCOS Resnet50 Caffe & 0.89 & 0.62 & 0.72 & 0.81 & 0.76 & 7 h-31 m \\ \hline ATSS Resnet101 & 0.92 & 0.66 & 0.81 & 0.78 & 0.8 & 7 h-14 m \\ \hline FoveaBox & 0.89 & 0.69 & 0.88 & 0.76 & 0.81 & 4 h-36 m \\ \hline SABL & 0.88 & 0.63 & 0.84 & 0.71 & 0.77 & 7 h-00 m \\ \hline \end{tabular}
\end{table} 
Table 3: Detection results of deep learning models for the presence of root dilaceration. mAP refers mAP when IOU is 0.5. A, P and R stand for accuracy, precision and recall, respectively. T-Time refers to how long it takes to train each model. superior to the others with 0.83. The training time for each model is also given in Table 1. Libra RCNN Resnext101 had the longest training time whereas RetinaNet Resnet50 was trained at the earliest. Figure 3 illustrates model predictions for root dilacerations with predicted bounding boxes and labels. Figure 3: Example of test process of root dilaceration detection. Model outputs/predictions are shown in rectangular boxes with corresponding labels. Two models were chosen for further analysis of the results, Cascade RCNN with ResNet101 as a backbone and RetinaNet with ResNet50. Precision-recall curve and training-validation losses were presented. The precision-recall curve is very useful to better understand where a corresponding model needs to be improved, in other words, in which step it fails.