## 4 Discussion  
While the detection of feature points using SIFT has been previously addressed in the literature, the central premise of this study is not solely limited to generating correspondences between examples of precursor cells. Instead, it focuses on developing a primitive learning approach based on feature points generated in real time during the processing of videos or images with distinctive values. Additionally, it aims to locate cells within a field of cervical cytology in both photos and videos in real time, specifying the type of visible cell that possesses the previously generated characteristics. In this work, deep learning models are not used, as the ultimate goal of this research, as well as future work, is to achieve effective learning with few examples, without the need for a large pre-existing set of example images. Although this approach presents some complexity, it is based on the following observation from the literature on cervical cancer: \"The process of detecting cells within a general microscopy field is complex because the Pap smear process does not, most of the time, exhibit adequate samples with proper staining, with a wide cellular sample where more than one cell can be distinguished in cervical cytology. Furthermore, as previously mentioned, cervical cytology is primarily a screening test for squamous cell carcinoma of the cervix and its precursors. Due to the wide spectrum of reactive cytomorphological changes, the criteria are not well defined and may lack reproducibility\". Nevertheless, Table 3 shows comparative values evaluating the proposed method against other strategies that predominantly employ deep learning techniques for the classification and detection of cervical cytology. [PERSON] [29] presents an innovative methodology that employs pre-trained deep neural network models for feature extraction and machine learning algorithms for cervical cancer classification, achieving an accuracy of 98.08% with ResNet-152, among other methodologies. Similarly, [PERSON] et al. [30] utilized transfer learning with pre-trained models on Pap smear images, evaluating 13 pre-trained CNN models on the Herlev dataset. DenseNet-201 demonstrated the best performance in terms of accuracy and efficiency, with excellent experimental results and minimal computing time. Likewise, [PERSON] [31] implemented a novel method for recognizing and classifying white blood cells in microscopic blood images, identifying them as healthy or affected by leukemia, as shown in Figure 20. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methodology** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F-Measure (\%)** \\ \hline Our Method & 98.34 & 98.3 & 98.2 & 98.05 \\ \hline ResNet-101 [29] & 98.08 & 98.64 & 98.64 & – \\ \hline ResNet-152 [29] & 98.98 & 99.64 & 99.42 & – \\ \hline DenseNet-201 [30] & 87.02 & 62.26 & 63.41 & 61.65 \\ \hline DenseNet-169 [30] & 85.26 & 57.55 & 58.54 & 61.65 \\ \hline Bd and DL [31] & 99.7 & 100 & 99.6 & 99.8 \\ \hline DeepCELL-v2 [32] & 92.717 & 91.545 & 89.707 & 90.475 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of methods with the state of the art. While the results are highly promising, it is noted that the quality of the datasets is crucial for the accurate identification of elements in cytology. Additionally, training deep learning models requires considerable time. Therefore, the idea of using deep learning models was dismissed in this investigation, whose central aim is to learn with few examples and enhance knowledge as key point correspondences are made. Although SIFT can be computationally demanding, the intention is to compute the descriptors once and make the correspondence much more efficient. This work serves as a foundation for future research to implement real-time learning techniques based on SIFT descriptors, given their robustness against various geometric transformations and changes in lighting. An important aspect to highlight is detecting precursor cells within a cytology sample.