## 3 Results  
The SIFT algorithm was evaluated using a dataset of 100 cervical cytology images with 1800 unique examples, and the results demonstrated a high level of accuracy and reliability in detecting cancerous cells. The algorithm achieved an accuracy of 98.34%, highlighting the overall capability of the algorithm to correctly classify both cancerous and non-cancerous cells. The precision was 98.3%, indicating that most of the cells identified as cancerous were indeed positive, reflecting the algorithm's ability to minimize false positives. The recall rate was 98.2%, meaning that the algorithm correctly identified the majority of cancerous cells present in the images, demonstrating its effectiveness in detecting true positives. The F-measure reached a value of 97.3%, a combined metric considering precision and recall. This high value indicates an optimal balance between precision and recall, underscoring the algorithm's robustness under diverse clinical conditions. Table 1 shows the aforementioned results. Additionally, the graph presented in Figure 12 illustrates the detection rate achieved by the SIFT algorithm from a knowledge base composed of 1800 unique examples of descriptors. This analysis was performed on 100 different frames, with a detection rate ranging from 94% to 99%. The consistency and robustness of the algorithm are reflected in the high detection rate maintained across all analyzed frames. Furthermore, considering that the SIFT algorithm can be highly computationally expensive, an analysis was conducted on the time required to generate keypoints in batches of 100 images with dimensions of 2400 \(\times\) 1080 pixels. The results indicate an average time of 0.57 s, as detailed in Table 2. \begin{table}
\begin{tabular}{c c} \hline \hline  & **Proposed Methodology** \\ \hline Cells & 500 \\ Accuracy & 98.34 \\ Precision & 98.3\% \\ Recall & 98.2\% \\ F-measure & 98.05\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detection of cells using SIFT in 100 cervical cytology images, utilizing 1800 unique examples of cells, from low-grade lesions. Figure 12: Time for keypoint extraction using SIFT in batches of 100 images. It was found that, in general, it is possible to extract features from a large image in a reasonable time. However, the same analysis was applied once the keypoint descriptors were generated and their search within a field of a cervical cytology was performed. The results indicate that the calculation time, depending on the image size, averages approximately 3 s, as shown in Figure 13.  
Nevertheless, if the descriptors are stored and reused without recalculating these values, the time is reduced to 0.0107 s. Additionally, if the image size is reduced by 20%, the execution time for point matching and cell detection significantly decreases to an average of 0.0099 s, as shown in Figure 14.  
This reduction in time is also attributed to the proposed implementation in Equation (12), which accelerates the localization of clusters among the descriptors in cervical cytology. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Batch**} & \multirow{2}{*}{**Number of Images**} & \multicolumn{2}{c}{**Average Keypoint**} \\  & & **Generation Time (seconds)** \\ \hline
1 & 100 & 0.59 \\ \hline
2 & 100 & 0.58 \\ \hline
3 & 100 & 0.60 \\ \hline
4 & 100 & 0.54 \\ \hline
5 & 100 & 0.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Time for keypoint extraction using SIFT in batches of 100 images. Figure 14: Time for keypoint detection and cell matching with saved keypoints and 20% image reduction. Figure 13: Time for keypoint detection and cell matching. This enhancement allows for real-time matching of keypoints in videos. The zero values indicate that no cell matching the stored keypoints was found. This result underscores the algorithm's effectiveness in identifying cervical cancer cells under various lighting conditions and image qualities.