## 1 Introduction
use a structured analysis or morphological approach to segment nuclei in Pap smear cell images, applying dilation and erosion operations based on shape, size, and structure [11]. Furthermore, [PERSON], [PERSON] et al. propose a cervical cancer prediction model using anomaly detection techniques and oversampling, combined with classification algorithms such as random forest [12]. [PERSON] et al. introduce a novel method to improve model generalization in cervical fluid-based cell detection by extracting causal features [13]. Furthermore, [PERSON] et al. present a semi-supervised metric learning method with dual alignment for cervical cancer cell detection, enhancing accuracy and generalization with limited data [14]. [PERSON] et al. propose a framework based on the Faster RCNN-FPN architecture for detecting abnormal cervical cells, enhancing the precision with deformable convolution layers and global context modules [15]. Similarly, [PERSON] et al. implement an improved YOLO algorithm to detect abnormal cervical cells, enhancing speed and accuracy in complex backgrounds. Techniques like S3 Pool and Focal Loss are added to optimize detection in challenging situations. The results show a precision (MAP) of 78.87%, surpassing other methods and improving overall sensitivity and accuracy [16]. [PERSON] et al. propose machine learning techniques such as logistic regression, SVM, and KNN combined with class balancing techniques to improve cervical cancer detection. High classification accuracy was achieved using random oversampling and SMOTETOMEK [17]. Additionally, [PERSON] and [PERSON] present a predictive model for cervical cancer using machine learning algorithms such as decision trees, logistic regression, and random forests. The model achieved 100% accuracy with random forests and decision trees [18]. In a work by [PERSON] et al., the authors describe the CervDetect approach, which uses machine learning algorithms to assess cervical cancer risk factors, achieving 93.6% accuracy using feature selection with random forests and shallow neural networks [19]. In a study by [PERSON] et al., a residual-within-residual design is employed to achieve faster inference and lower memory demands, thereby improving image clarity and optimizing the peak signal-to-noise ratio (PSNR). DRFDCAN outperforms frameworks like RFDN by balancing computational efficiency with high-fidelity image reconstruction, which is crucial for accurate medical diagnoses [20]. Given the subjective limitations of analysis and related works, this study proposes the implementation of computer vision algorithms, specifically SIFT (Scale-Invariant Feature Transform) descriptors, for the morphological detection of precursor or cancerous cells from a single example. A detailed analysis is conducted on the generation of features and specific descriptors for cervical cytology images, generated from slides provided by the State Public Health Laboratory of Michoacan, Mexico. Although well-established datasets exist in the literature, the analysis process of the Pap smear method and the generation of cytological samples has shown that, in numerous cases, the samples are inadequate but still require analysis. Therefore, the images used in this study come from a diverse set of slides, including positive, negative, and a variety of stains and qualities. Similarly, the cell detection process can be applied to both images and videos. This approach aims to improve the accuracy and efficiency in detecting cancerous cells, providing specialists with a tool for the diagnosis and monitoring of cervical cancer.