## 3 Materials and Methods
### Proposed YOLO  
The YOLO model is based on a CNN network approach that is capable of performing object detection in images, identifying both the classes and locations of objects in a single pass through the network. The YOLO training process involved teaching the neural network to detect objects in images based on a labeled dataset, where each image contains objects and their previously annotated bounding boxes. Input images are normalized and resized to a fixed size, and the labels for each image include the bounding boxes of the objects and their respective classes. YOLO divides the input image into a grid of \(N\times N\) cells, by dividing the image into \(N\) horizontal and vertical segments. Each cell in the grid must be classified using a CNN so that grouping these cells by class allows for the creation of bounding boxes surrounding detected objects. The output of the network is a set of bounding boxes with respective predicted classes and confidence degrees. A non-maximum suppression process is applied to the output to  
Figure 5: Uniform distribution of descriptors in the image with different distances and different sizes for the region of interest in terms of pixels. (**a**) Size 50, distance 25. (**b**) Size 50, distance 50. (**c**) Size 100, distance 50. (**d**) Size 25, distance 5. Figure 6: Matching keypoints by correspondence between descriptors. On the left is the reference brain (test image), with the points of interest chosen manually. On the right is the test image. The lines, automatically generated by the algorithm, connect the points with the greatest similarity between the images. remove redundancies and overlapping cases in order to present a single bounding box per detected object. The YOLO-based method has several versions that present improvements in each stage of image processing. In this work, version 8 of this method was used, specifically the average model of this version. The choice is justified because it is one of the most current versions and has presented good results in other reputable databases in the area. The average model has a total of 319 layers and 23.2 million parameters within its architecture, which is subdivided into backbone, neck, and head components. These subdivisions, respectively, perform the extraction of main features of the image, combination and refinement of features at different scales to identify objects of different sizes, and finally the prediction of the class to which the object belongs and its bounding box. In all cases, the optimizer used was AdamW, configured with a learning rate of 0.001667, a momentum factor of 0.9, and the application of Weight Decay. Regarding the momentum, it utilizes 90% of the previous gradient value and 10% of the current one to update the weights. Additionally, the Weight Decay helps to prevent excessively large weight values, contributing to more stable and generalizable models. The greater accuracy of YOLO is related to the number of parameters that increases with the improvement of the architecture in the different versions of the model. In this way, both the structure and the number of parameters contribute to greater accuracy of the model [29]. To implement this method, the online environment Google Collaboratory was used, which provides a configuration consisting of an Intel Xeon CPU with a core and clock of 2 GHz, 13 GB of RAM, 78 GB of storage and an Nvidia Tesla T4 video card with 15 GB of VRAM. In addition, the Ultralytics library, from the Python programming language, was used to use the average YoloV8 model. The images used to train and validate the model were labeled by experts with identification of the key points for drawing the line that identifies the cutting plane. From these values, 50-pixel bounding boxes were created, where the center coordinate would be the same as the annotations, thus demarcating the regions of the image that the YOLO method should locate.