利用傅里叶变换红外光谱（FTIR）数据对宫颈癌细胞进行分类。

Classification of cervical cancer cells using FTIR data.

作者信息

Njoroge Erick, Alty Stephen R, Gani Mahbub R, Alkatib Maha

机构信息

King's College, London, Centre for Digital Signal Processing Research, Strand, London, UK.

出版信息

Conf Proc IEEE Eng Med Biol Soc. 2006;2006:5338-41. doi: 10.1109/IEMBS.2006.260024.

DOI:10.1109/IEMBS.2006.260024

PMID:17945893

Abstract

High false-negative rates of the Papanicolauo (so-called 'Pap') smear test and the shortage of colposcopists have led to the desire to find alternative non-expert (automated) approaches for accurately testing cervical smears for signs of cancer. Fourier-Transform Infra-Red (FTIR) spectroscopy has been shown to offer the potential for improving the accuracy (i.e. sensitivity and specificity) of these tests. This paper details the application of the machine learning methodology of Support Vector Machines (SVM) using FTIR data to enhance and improve upon the standard Pap test. A cohort of 53 subjects was used to test the veracity of both the Pap smear results and the FTIR based classifier against the findings of the colposcopists. The Pap test achieved an overall classification of 43 %, whereas our method achieved a rate of 72%

摘要

巴氏涂片检查的高假阴性率以及阴道镜检查医师的短缺，促使人们希望找到替代的非专业（自动化）方法，以准确检测宫颈涂片是否存在癌症迹象。傅里叶变换红外（FTIR）光谱已被证明有潜力提高这些检测的准确性（即灵敏度和特异性）。本文详细介绍了使用支持向量机（SVM）机器学习方法结合FTIR数据来增强和改进标准巴氏试验的应用。使用一组53名受试者，根据阴道镜检查医师的诊断结果来检验巴氏涂片结果和基于FTIR的分类器的准确性。巴氏试验的总体分类准确率为43%，而我们的方法达到了72%的准确率。