## 5 Conclusions  
This study demonstrates the potential of using SIFT descriptors for the detection and monitoring of precancerous cervical cells in video sequences captured by mobile devices. By analyzing 100 digital images of Papanicolaou tests provided by the State Public Health Laboratory of Michoacan, Mexico, and more than 1800 unique examples of precancerous cells, the methodology achieved an accuracy of 98.34%, a precision of 98.3%, a recall rate of 98.2%, and an F-measure of 98.05%.  
These results underscore the efficacy of SIFT descriptors in providing real-time matching of precancerous cells, thereby improving the accuracy and efficiency of the Papanicolaou test. The meticulous optimization of these methods for real-time analysis indicates a promising advancement in the early detection of cervical cancer, offering a robust tool to enhance patient survival rates through early and accurate diagnosis.  
Figure 20: Comparison of methods with the state of the art.  
The implementation of the SIFT algorithm, along with the search for a new homography based on characteristic points and descriptors, constitutes an innovative strategy for knowledge acquisition.  
Each set of descriptors generated by SIFT can be considered a \"primitive memory\" of the cell, as it is capable of performing recognitions based on a single example. This approach is particularly relevant as it allows for effective specialization without relying on deep learning techniques, which typically require intensive processes and a considerable volume of data to achieve adequate learning. The application of SIFT seeks to emulate the human capacity for learning from a single example by extracting unique and distinctive features from it. Similarly, future work envisions the generalization of this primitive memory through the tracking of the cell in video sequences.  
Nonetheless, it is imperative to recognize that additional developments, tests, and applications in this field are still required. Samples can exhibit significant variations between individuals, and the specific conditions of each patient can introduce a considerable diversity of modifications in the cells. Moreover, medical treatments can alter cellular characteristics, necessitating constant system adaptation to maintain its accuracy and efficacy. Therefore, while the SIFT-based approach and descriptor generation show notable potential, it is essential to continue with exhaustive research to validate and improve this methodology. The inherent variability of human samples and the diverse clinical conditions imply the need for additional studies to ensure the robustness and reliability of the system across a wide range of scenarios. This field of research remains promising, and with proper development and implementation, it could significantly transform diagnostic practices in cytology and other biomedical contexts.  
Conceptualization, J.E.A.-C. and A.d.C.T.-A.; software, J.E.A.-C.; validation, J.C.O.-R. and R.M.-P.; writing--original draft preparation, J.E.A.-C.; writing--review and editing, A.d.C.T.-A.; supervision, A.d.C.T.-A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Tecnologico Nacional de Mexico.  
This study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Ethics Committee of TecNM/Instituto Tecnologico de Morelia.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data available by request.  
The authors would like to express their gratitude to the State Public Health Laboratory of Michoacan, particularly the oncology department, for providing us with cervical cytology samples and their ongoing guidance in identifying precursor cervical cancer cells. Furthermore, we dedicate this work to every woman who faces this condition.  
The authors declare no conflicts of interest.