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

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Algorithms Pub Date : 2024-07-12 DOI:10.3390/a17070309

J. E. Alcaraz-Chavez, A. Téllez-Anguiano, Juan C. Olivares-Rojas, R. Martínez-Parrales

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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.

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宫颈癌前体细胞的实时跟踪和检测：利用移动视频序列中的 SIFT 描述符加强早期诊断

宫颈癌是导致全球妇女死亡的主要原因之一，这说明早期检测对确保患者存活至关重要。虽然巴氏涂片检测被广泛使用，但其有效性却因细胞学分析固有的主观性而受到影响，影响了其灵敏度和特异性。本研究介绍了一种创新方法，利用移动设备拍摄的视频序列中的 SIFT 描述符来检测和跟踪宫颈癌前体细胞。研究人员对墨西哥米却肯州公共卫生实验室提供的 100 多张巴氏涂片数字图像以及 1800 多个独特的宫颈癌前体细胞实例进行了分析。SIFT 描述符实现了前体细胞的实时对应，结果显示准确率为 98.34%，精确率为 98.3%，恢复率为 98.2%，F 值为 98.05%。这些方法针对实时分析进行了细致的优化，在提高早期宫颈癌检测中巴氏涂片检测的准确性和效率方面展现出巨大的潜力。

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