## 3 Materials and Methods  
The generation of descriptors and later recognition of specific anatomical structures of the brain in MRI images are the core of the present work. This process involves several critical steps, beginning with the acquisition of high-quality MRI scans that capture detailed images of the brain's internal structures. Once these images are obtained, advanced image processing techniques are applied to enhance the visibility of anatomical features, ensuring that even the smallest details are accurately represented. In this section, we briefly explain the formation of magnetic resonance images (MRIs) used in the work through the algorithm proposed by [PERSON] [10], called SIFT, and the YOLO neural network [26].  
The formation of MRIs is based on the principles of nuclear magnetic resonance, which exploits the magnetic properties of certain atomic nuclei. When placed in a strong magnetic field and exposed to a specific frequency of radio waves, these nuclei produce signals that can be detected and transformed into images. This imaging technique provides exceptional contrast between different types of soft tissue, making it particularly useful for brain imaging. The clarity and detail of MRIs are crucial for the accurate generation of descriptors, which are unique numerical representations of anatomical structures.  
The SIFT (Scale-Invariant Feature Transform) algorithm, proposed by [PERSON], plays a pivotal role in this work by enabling the detection and description of local features in MRIs. SIFT is designed to be invariant to scale and rotation, which means it can robustly identify features regardless of the image's size or orientation. This property is essential for the reliable recognition of anatomical structures, as it ensures that the descriptors generated are consistent across different imaging conditions. By applying SIFT, we can extract distinctive, high-quality features from MRIs, facilitating the accurate identification and analysis of specific brain structures.  
The YOLO (You Only Look Once) neural network is an evolution of convolutional neural network (CNN) models for object detection in images. Unlike models based on the costly selection of many proposed regions, the YOLO model partitions the image into a grid and classifies each segment to directly build proposed regions by grouping the segments of the same class. This strategy achieved high speed for classification stages, becoming popular in object detection tasks.  
The comparison of the SIFT and YOLO models in object detection tasks needs to take into account some aspects that may make one or the other model more advantageous depending on the scenario. While in the detection and classification stages both are capable of presenting results in real time, the training stage of the YOLO model requires more time, more computational resources, and a larger database compared to SIFT. On the other hand, the additional cost of training with YOLO has the advantage of offering a model with greater accuracy in the detection and classification phase. Thus, the choice of one model or the other becomes a question of cost-benefit depending on the available resources and accuracy requirements.