# Root Dilaceration Using Deep Learning:
## 2 Materials and Methods
### Object Detection and Detectors  
This section briefly explains the object detection task and introduces the deep-learning-based detectors used in this work. The object detection task basically tries to find where object locations are, and which class object belongs to the images. Compared to traditional techniques using hand-crafted features, deep-learning-based approaches take advantage of hierarchical feature representation, high learning, and expressing capability and joint optimization of classification and localization in a multitask learning approach thanks to a hierarchical multistage deep structure. The two types of deep-learning-based generic object detection frameworks are region proposal-based techniques and regression/classification-based techniques. The former technique generates region proposals and later classifies each proposal into object classes whereas the latter approximates object locations directly without choosing interested parts. R-CNN, Fast R-CNN, and Faster R-CNN are region proposal-based models; on the other hand, YOLO, SSD, and RetinaNet are regression-based methods. There are 19 state-of-the-art deep learning detection models including two-stage and one-stage detectors. Faster RCNN and RCNN are two-stage techniques that need a backbone as a feature extractor whereas SSD, YOLO, and RetinaNet perform detection in a single-step. ResNet-50 and ResNet-101 and DarkNet-53 CNNs are used as a backbone for detectors. In addition to detectors, models with new frameworks or approaches to improve existing performance were also used. The training batch size was 8. The stochastic gradient descent (SGD) optimization algorithm was used with a learning rate of 0.01, momentum of 0.9 and weight decay of 0.0001. A step learning rate scheduler was used as the optimizer. Models were evaluated on mean average precision, accuracy, precision and recall metrics. Outputs were analyzed using precision-recall curves for the best performing models. Figure 1: Flowchart of the study. Collected data were refined according to criteria, then labelled and fed into deep learning models, resulting in their predictions in detecting root dilacerations. The Cascade R-CNN model was developed from improvements on the Faster R-CNN model [26]. It outperforms Faster R-CNN in object detection and Mask R-CNN in the segmentation task. In this study, a ResNet101 backbone was used for this model. Faster R-CNN was obtained with the improvements on the Fast R-CNN model [27]. It combines the Region Proposal Network (RPN) and Fast R-CNN for object detection. A ResNet101 backbone was used for this model. Figure 2: Example PRs with root dilacerations. Red rectangular boxes indicate dilacerated roots that were also used as ground truths. Arrows show dilacerated roots. RetinaNet is a one-stage object detector developed by [PERSON] et al. [28]. It is a single network including a backbone and two task-specific subnetworks. It uses a focal loss function for class imbalances. ResNet50 was used for this model. YOLOv3 is an another one-stage model [29]. It has been updated based on the previous version with its feature extractor that has skip connections and three prediction heads that are each used for image processing at different spatial compressions. The SSD (Single Shot MultiBox Detector) model is a one-stage deep learning model developed by [PERSON] et al. [30]. The significant improvement is the speed that is provided by removing bounding box proposals and the feature resampling stage. RegNetX is a convolutional network design space with simple, regular models that parametrize populations of networks. RegNetX was implemented with Faster R-CNN. Libra R-CNN is an object detection model focused on the training process [31]. It is a simple and effective approach for balancing datasets by integrating IoU sampling, a feature pyramid, and L1 loss. ResNeXt-101-FPN was used as a backbone. Deformable convolutional networks, also known as deformable ConvNets, is a deep learning model created by [PERSON] et al. [32].