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Article

Real-Time Tracking and Detection of Cervical Cancer Precursor Cells: Leveraging SIFT Descriptors in Mobile Video Sequences for Enhanced Early Diagnosis

by
Jesus Eduardo Alcaraz-Chavez
,
Adriana del Carmen Téllez-Anguiano
*,
Juan Carlos Olivares-Rojas
and
Ricardo Martínez-Parrales
DEPI, Tecnológico Nacional de México/Instituto Tecnológico de Morelia, Av. Tecnológico No. 1500, Col. Lomas de Santiguito, Morelia 58120, Michoacán, Mexico
*
Author to whom correspondence should be addressed.
Algorithms 2024, 17(7), 309; https://doi.org/10.3390/a17070309
Submission received: 14 June 2024 / Revised: 9 July 2024 / Accepted: 10 July 2024 / Published: 12 July 2024
(This article belongs to the Special Issue Recent Advances in Algorithms for Computer Vision Applications)

Abstract

Cervical cancer ranks among the leading causes of mortality in women worldwide, underscoring the critical need for early detection to ensure patient survival. While the Pap smear test is widely used, its effectiveness is hampered by the inherent subjectivity of cytological analysis, impacting its sensitivity and specificity. This study introduces an innovative methodology for detecting and tracking precursor cervical cancer cells using SIFT descriptors in video sequences captured with mobile devices. More than one hundred digital images were analyzed from Papanicolaou smears provided by the State Public Health Laboratory of Michoacán, Mexico, along with over 1800 unique examples of cervical cancer precursor cells. SIFT descriptors enabled real-time correspondence of precursor cells, yielding results demonstrating 98.34% accuracy, 98.3% precision, 98.2% recovery rate, and an F-measure of 98.05%. These methods were meticulously optimized for real-time analysis, showcasing significant potential to enhance the accuracy and efficiency of the Pap smear test in early cervical cancer detection.
Keywords: SIFT; keypoints; Laplacian of Gaussian; cervical cytology; cancer diagnosis SIFT; keypoints; Laplacian of Gaussian; cervical cytology; cancer diagnosis

Share and Cite

MDPI and ACS Style

Alcaraz-Chavez, J.E.; Téllez-Anguiano, A.d.C.; Olivares-Rojas, J.C.; Martínez-Parrales, R. Real-Time Tracking and Detection of Cervical Cancer Precursor Cells: Leveraging SIFT Descriptors in Mobile Video Sequences for Enhanced Early Diagnosis. Algorithms 2024, 17, 309. https://doi.org/10.3390/a17070309

AMA Style

Alcaraz-Chavez JE, Téllez-Anguiano AdC, Olivares-Rojas JC, Martínez-Parrales R. Real-Time Tracking and Detection of Cervical Cancer Precursor Cells: Leveraging SIFT Descriptors in Mobile Video Sequences for Enhanced Early Diagnosis. Algorithms. 2024; 17(7):309. https://doi.org/10.3390/a17070309

Chicago/Turabian Style

Alcaraz-Chavez, Jesus Eduardo, Adriana del Carmen Téllez-Anguiano, Juan Carlos Olivares-Rojas, and Ricardo Martínez-Parrales. 2024. "Real-Time Tracking and Detection of Cervical Cancer Precursor Cells: Leveraging SIFT Descriptors in Mobile Video Sequences for Enhanced Early Diagnosis" Algorithms 17, no. 7: 309. https://doi.org/10.3390/a17070309

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