Classification of cervical cancer cells using FTRR data

E Njoroge; S R Alty; M R Gani; M Alkatib

Classification of cervical cancer cells using FTRR data

E Njoroge, S R Alty, M R Gani, M Alkatib

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

Abstract

High false-negative rates of the Papanicolauo (socalled '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%

Original language	English
Title of host publication	2006 28Th Annual International Conference of the Ieee Engineering in Vols 1-15
Place of Publication	NEW YORK
Publisher	IEEE
Pages	1286 - 1289
Number of pages	4
ISBN (Print)	978-1-4244-0032-4
Publication status	Published - 2006
Event	28th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society - New York, NY Duration: 1 Jan 2006 → …

Conference

Conference	28th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society
City	New York, NY
Period	1/01/2006 → …

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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title = "Classification of cervical cancer cells using FTRR data",

abstract = "High false-negative rates of the Papanicolauo (socalled '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%",

author = "E Njoroge and Alty, {S R} and Gani, {M R} and M Alkatib",

year = "2006",

language = "English",

isbn = "978-1-4244-0032-4",

pages = "1286 -- 1289",

booktitle = "2006 28Th Annual International Conference of the Ieee Engineering in Vols 1-15",

publisher = "IEEE",

note = "28th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society ; Conference date: 01-01-2006",

}

TY - CHAP

T1 - Classification of cervical cancer cells using FTRR data

AU - Njoroge, E

AU - Alty, S R

AU - Gani, M R

AU - Alkatib, M

PY - 2006

Y1 - 2006

N2 - High false-negative rates of the Papanicolauo (socalled '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%

AB - High false-negative rates of the Papanicolauo (socalled '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%

M3 - Conference paper

SN - 978-1-4244-0032-4

SP - 1286

EP - 1289

BT - 2006 28Th Annual International Conference of the Ieee Engineering in Vols 1-15

PB - IEEE

CY - NEW YORK

T2 - 28th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

Y2 - 1 January 2006

ER -

Classification of cervical cancer cells using FTRR data

Abstract

Conference

UN SDGs

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