## 4 Proposed Methodology
### Ensemble Learning
If the IoU is greater than a predefined threshold (e.g., 0.5), discard the box with the lower score. 4. Repeat until no more boxes are left to compare. Where NMS uses the following practical considerations:

* IoU Threshold: A lower threshold (e.g., 0.3) leads to more aggressive suppression, whereas a higher threshold (e.g., 0.7) allows more overlap. * Speed-Accuracy Tradeoff: While NMS helps remove redundant detections, it may also suppress valid ones in certain cases, especially with small IoU thresholds. Balancing precision and recall is important. * Class-Specific NMS: In some models, NMS is applied separately for each class, preventing boxes from being suppressed across different object categories. YOLO8 improves upon previous versions by optimizing these components for better accuracy and speed. It is considered one of the latest versions of the popular YOLO (You Only Look Once) family of algorithms. YOLO8 has no official paper but boasts higher accuracy and faster speed. It employs a backbone network (usually a convolutional neural network, CNN) to extract features from the image. The backbone, based on CSPDarknet53 (cross-Stage Partial network), has 53 layers that talk to each other better for a clearer understanding of images. This part helps focus on different parts of a picture and figure out which features matter the most for finding objects. CSPDarknet53 acts as a backbone network, providing a robust and efficient method for extracting objects from image input. The integration in YOLO8 helps strike a balance between computational efficiency and detection accuracy, making it suitable for real-time object recognition applications. YOLO8 is also good at spotting objects of different sizes in pictures, thanks to its feature pyramid network as shown in Figure 1 phase Backbone Process [30]. Also, object detection localizes an object within an image by drawing bounding boxes. The implementation needs you to declare the model as yolo8n.pt for object detection using the nano variant. You do not need to add any suffix to use YOLO8 for detection. YOLO8 uses anchor-free detection, where the model directly predicts an object's center rather than calculating the offset from a predefined anchor box. Anchor boxes are predefined boxes with specific heights and widths, designed to detect object classes at particular scales and aspect ratios. During detection, they are distributed throughout the image and chosen according to object sizes in the training dataset [30]. After preparing the dataset as explained in the following subsection, we apply YOLO8 through the steps of Algorithm 1. ## 5 Performance Evaluation

The proposed integrated model is tested on a different set of experiments for validation. Many random experiments with real-time datasets have been conducted. This section presents the platform, how to set up the investigation, the results, and the discussion. For comparison, the proposed model is compared with three competitive algorithms. ### Panoramic X-ray Dataset (Collect Data)

According to [31] and various other sources, the detector model's label data format should have a particular structure as shown in Table 1. Each object's coordinates and class index are included in the data; these are normalized according to the image's width and height. For an \(864\times 1188\) image, each item is represented by a single line in the text file, which has the format \((x1/864,y1/1188,x2/864,y2/1188)\). The class index for the \"caries\" class is one, whereas the class index for the \"no-caries\" class is zero. By ensuring that the coordinates are scaled about the image dimensions, this normalization guarantees that the detector model can handle them. Label data, often referred to as ground truth data, provide annotations that specify the presence and location of objects of interest in an image. ### Performance Metrics

The performance of the image detection models was evaluated using accuracy as in (13).