A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images
Background Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious...
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| Published in | Biomedical engineering online Vol. 18; no. 1; pp. 16 - 22 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
12.02.2019
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1475-925X 1475-925X |
| DOI | 10.1186/s12938-019-0634-5 |
Cover
| Abstract | Background
Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images.
Method
Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm.
Results
The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%’, ‘97.64%, 98.08% and 97.16%’ and ‘95.00%, 100% and 90.00%’ were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab.
Conclusions
The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5–10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies. |
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| AbstractList | Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images. Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm. The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of '98.88%, 99.28% and 97.47%', '97.64%, 98.08% and 97.16%' and '95.00%, 100% and 90.00%' were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab. The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5-10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies. Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images. Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm. The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of '98.88%, 99.28% and 97.47%', '97.64%, 98.08% and 97.16%' and '95.00%, 100% and 90.00%' were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab. The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5-10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies. Background Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images. Method Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm. Results The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%’, ‘97.64%, 98.08% and 97.16%’ and ‘95.00%, 100% and 90.00%’ were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab. Conclusions The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5–10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies. Background Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images. Method Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm. Results The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of '98.88%, 99.28% and 97.47%', '97.64%, 98.08% and 97.16%' and '95.00%, 100% and 90.00%' were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab. Conclusions The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5-10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies. Keywords: Pap-smear, Fuzzy C-means, Cervical cancer Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images.BACKGROUNDCervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images.Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm.METHODScene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm.The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of '98.88%, 99.28% and 97.47%', '97.64%, 98.08% and 97.16%' and '95.00%, 100% and 90.00%' were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab.RESULTSThe evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of '98.88%, 99.28% and 97.47%', '97.64%, 98.08% and 97.16%' and '95.00%, 100% and 90.00%' were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab.The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5-10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.CONCLUSIONSThe major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5-10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies. Background Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images. Method Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm. Results The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%’, ‘97.64%, 98.08% and 97.16%’ and ‘95.00%, 100% and 90.00%’ were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab. Conclusions The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5–10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies. Abstract Background Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images. Method Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm. Results The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%’, ‘97.64%, 98.08% and 97.16%’ and ‘95.00%, 100% and 90.00%’ were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab. Conclusions The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5–10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies. |
| ArticleNumber | 16 |
| Audience | Academic |
| Author | Ware, Andrew William, Wasswa Basaza-Ejiri, Annabella Habinka Obungoloch, Johnes |
| Author_xml | – sequence: 1 givenname: Wasswa orcidid: 0000-0002-0202-1230 surname: William fullname: William, Wasswa email: wwasswa@must.ac.ug organization: Department of Biomedical Sciences and Engineering, Mbarara University of Science and Technology – sequence: 2 givenname: Andrew surname: Ware fullname: Ware, Andrew organization: Faculty of Computing, Engineering and Science, University of South Wales, Prifysgol – sequence: 3 givenname: Annabella Habinka surname: Basaza-Ejiri fullname: Basaza-Ejiri, Annabella Habinka organization: College of Computing and Engineering, St. Augustine International University – sequence: 4 givenname: Johnes surname: Obungoloch fullname: Obungoloch, Johnes organization: Department of Biomedical Sciences and Engineering, Mbarara University of Science and Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30755214$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.cmpb.2018.05.034 10.1109/34.1000236 10.1111/j.1749-6632.1962.tb34660.x 10.17485/ijst/2016/v9i28/98380 10.1159/000326353 10.5772/8912 10.1016/j.neucom.2017.04.053 10.1155/2016/9535027 10.1016/j.compbiomed.2008.11.006 10.1159/000326725 10.1016/B978-0-12-394447-4.30036-0 10.1007/s11760-015-0791-3 10.1109/ICEE.2009.5173186 10.1016/j.gore.2017.01.009 10.1177/0272989X9101100203 10.1016/j.fss.2007.08.016 10.23919/SAIEE.2005.9488092 10.1016/j.isprsjprs.2009.06.004 10.1093/bioinformatics/btx180 10.1109/JBHI.2017.2705583 10.1097/LGT.0b013e3182320f0c 10.1109/ISBI.2008.4541170 10.1016/j.eswa.2015.05.014 10.1142/S0218488503002387 10.1109/IJCNN.2012.6252801 10.1016/j.bbe.2016.11.006 10.1023/A:1010933404324 10.1002/int.1030 10.2355/tetsutohagane.TETSU-2016-072 10.1016/j.aogh.2014.09.014 10.1109/ICASSP.2002.5745451 10.1002/cncr.20720 10.1371/journal.pone.0029740 10.1016/S0923-5965(00)00019-9 10.1016/j.cmpb.2013.12.012 10.1109/TFUZZ.2012.2201485 10.21437/Interspeech.2014-226 10.3322/caac.21262 10.1155/2014/842037 10.1016/B978-0-12-398358-9.00007-0 10.1016/j.cmpb.2016.10.001 10.1007/s11912-004-0083-5 10.1002/cyto.990010305 10.1016/j.cmpb.2013.02.008 10.1016/j.procs.2016.06.029 10.1155/2007/678793 10.1155/2015/457906 10.1038/nrc798 10.1016/S0968-8080(08)32415-X |
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| DOI | 10.1186/s12938-019-0634-5 |
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| Keywords | Fuzzy C-means Pap-smear Cervical cancer |
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| References | K Bora (634_CR64) 2017; 138 634_CR50 N Tanaka (634_CR14) 1987; 9 634_CR10 S Opricovic (634_CR57) 2003; 11 K Bartyzel (634_CR37) 2016; 10 634_CR59 634_CR58 EG Diacumakos (634_CR13) 1962; 97 J Soto (634_CR60) 2008; 159 I Arganda-carreras (634_CR65) 2016; 33 S Biswas (634_CR39) 2016; 89 T Chankong (634_CR22) 2014; 113 C Nakisige (634_CR2) 2017; 20 R Sankaranarayanan (634_CR26) 2014; 80 RI Anorlu (634_CR5) 2008; 16 T Blaschke (634_CR44) 2010; 65 L Zhang (634_CR62) 2017; 21 N Ampazis (634_CR25) 2004; 3025 S Das (634_CR51) 2001; 1 634_CR40 S Roychowdhury (634_CR55) 2001; 16 J Su (634_CR19) 2016; 2016 DG Fryback (634_CR29) 1991; 11 634_CR41 634_CR48 C Duanggate (634_CR9) 2008; 1 L Breiman (634_CR52) 2001; 45 634_CR46 634_CR45 LA Torre (634_CR1) 2015; 65 TF Kardos (634_CR18) 2004; 102 H Mabeya (634_CR7) 2012; 16 C Kanan (634_CR34) 2012; 7 International Agency for Research on Cancer (634_CR6) 2011 A Stetco (634_CR56) 2015; 42 WD Tench (634_CR11) 2002; 46 C Bergeron (634_CR12) 2000; 44 N Wentzensen (634_CR36) 2007; 23 634_CR32 DJ Wiley (634_CR4) 2004; 6 634_CR31 J Joseph (634_CR33) 2017; 37 I Arganda-Carreras (634_CR27) 2017; 33 634_CR30 H Zur Hausen (634_CR35) 2002; 5 WY Kim (634_CR49) 2000; 16 JJ Francis (634_CR38) 2005; 96 D Comaniciu (634_CR42) 2002; 24 Y Marinakis (634_CR63) 2009; 39 P Malm (634_CR43) 2013; 111 R Montero (634_CR47) 2009; 4 F Busetti (634_CR53) 2003 634_CR61 634_CR21 DJ Zahniser (634_CR15) 1980; 1 634_CR20 I Journal (634_CR24) 2012; 45 E Bengtsson (634_CR8) 2014; 2014 M Taguchi (634_CR66) 2017; 103 MM Mafarja (634_CR28) 2017; 260 634_CR68 634_CR67 R Erhardt (634_CR16) 1980; 2 M Chivukula (634_CR17) 2007; 4 World Health Organization (634_CR3) 2013 TC Havens (634_CR54) 2012; 20 J Talukdar (634_CR23) 2013; 3 |
| References_xml | – ident: 634_CR10 doi: 10.1016/j.cmpb.2018.05.034 – volume: 24 start-page: 603 year: 2002 ident: 634_CR42 publication-title: IEEE Trans Pattern Anal Mach Intell. doi: 10.1109/34.1000236 – volume: 97 start-page: 498 year: 1962 ident: 634_CR13 publication-title: Ann N Y Acad Sci. doi: 10.1111/j.1749-6632.1962.tb34660.x – ident: 634_CR32 – ident: 634_CR61 – ident: 634_CR59 – ident: 634_CR20 doi: 10.17485/ijst/2016/v9i28/98380 – volume: 44 start-page: 151 year: 2000 ident: 634_CR12 publication-title: Acta Cytol. doi: 10.1159/000326353 – volume: 4 start-page: 6 year: 2007 ident: 634_CR17 publication-title: Cyto J. – ident: 634_CR48 doi: 10.5772/8912 – volume: 45 start-page: 35 issue: 20 year: 2012 ident: 634_CR24 publication-title: Int J Comput Appl – volume: 260 start-page: 302 year: 2017 ident: 634_CR28 publication-title: Neurocomputing. doi: 10.1016/j.neucom.2017.04.053 – volume: 2016 start-page: 9535027 year: 2016 ident: 634_CR19 publication-title: Anal Cell Pathol doi: 10.1155/2016/9535027 – volume: 39 start-page: 69 issue: 1 year: 2009 ident: 634_CR63 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2008.11.006 – volume: 46 start-page: 296 year: 2002 ident: 634_CR11 publication-title: Acta Cytol. doi: 10.1159/000326725 – ident: 634_CR68 doi: 10.1016/B978-0-12-394447-4.30036-0 – volume: 10 start-page: 663 issue: 4 year: 2016 ident: 634_CR37 publication-title: Signal Image Video Process. doi: 10.1007/s11760-015-0791-3 – volume: 3025 start-page: 230 year: 2004 ident: 634_CR25 publication-title: Lect Notes Artif Intell. – ident: 634_CR58 doi: 10.1109/ICEE.2009.5173186 – volume: 20 start-page: 37 year: 2017 ident: 634_CR2 publication-title: Gynecol Oncol Rep. doi: 10.1016/j.gore.2017.01.009 – volume: 11 start-page: 88 year: 1991 ident: 634_CR29 publication-title: Med Decis Mak. doi: 10.1177/0272989X9101100203 – ident: 634_CR50 – volume: 159 start-page: 406 year: 2008 ident: 634_CR60 publication-title: Fuzzy Sets and Systems. doi: 10.1016/j.fss.2007.08.016 – volume: 96 start-page: 106 year: 2005 ident: 634_CR38 publication-title: SAIEE Afr Res J. doi: 10.23919/SAIEE.2005.9488092 – volume: 2 start-page: 25 year: 1980 ident: 634_CR16 publication-title: Anal Quant Cytol. – volume: 65 start-page: 2 year: 2010 ident: 634_CR44 publication-title: ISPRS J Photogrammetry Remote Sens. doi: 10.1016/j.isprsjprs.2009.06.004 – volume: 33 start-page: 2424 year: 2016 ident: 634_CR65 publication-title: Bioinformatics. doi: 10.1093/bioinformatics/btx180 – volume: 21 start-page: 1633 year: 2017 ident: 634_CR62 publication-title: IEEE J Biomed Heal Informatics. doi: 10.1109/JBHI.2017.2705583 – volume: 16 start-page: 92 year: 2012 ident: 634_CR7 publication-title: J Low Genit Tract Dis. doi: 10.1097/LGT.0b013e3182320f0c – ident: 634_CR40 doi: 10.1109/ISBI.2008.4541170 – volume: 1 start-page: 74 year: 2001 ident: 634_CR51 publication-title: Engineering. – volume: 1 start-page: 2 year: 2008 ident: 634_CR9 publication-title: Public Health. – volume: 42 start-page: 7541 year: 2015 ident: 634_CR56 publication-title: Expert Syst Appl. doi: 10.1016/j.eswa.2015.05.014 – volume: 11 start-page: 635 year: 2003 ident: 634_CR57 publication-title: Int J Uncertainty Fuzziness Knowl Based Syst doi: 10.1142/S0218488503002387 – ident: 634_CR67 doi: 10.1109/IJCNN.2012.6252801 – volume-title: WHO guidelines for screening and treatment of precancerous lesions for cervical cancer prevention year: 2013 ident: 634_CR3 – volume: 37 start-page: 489 year: 2017 ident: 634_CR33 publication-title: Biocybern Biomed Eng. doi: 10.1016/j.bbe.2016.11.006 – ident: 634_CR30 – volume: 4 start-page: 1305 year: 2009 ident: 634_CR47 publication-title: Int Math Forum. – volume-title: Simulated annealing overview year: 2003 ident: 634_CR53 – volume: 45 start-page: 5 year: 2001 ident: 634_CR52 publication-title: Mach Learn. doi: 10.1023/A:1010933404324 – volume: 16 start-page: 679 year: 2001 ident: 634_CR55 publication-title: Int J Intell Syst. doi: 10.1002/int.1030 – volume: 33 start-page: 2424 issue: 15 year: 2017 ident: 634_CR27 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btx180 – volume: 103 start-page: 142 issue: 3 year: 2017 ident: 634_CR66 publication-title: Tetsu-to-Hagane. doi: 10.2355/tetsutohagane.TETSU-2016-072 – volume: 80 start-page: 412 year: 2014 ident: 634_CR26 publication-title: Ann of Glob Health. doi: 10.1016/j.aogh.2014.09.014 – ident: 634_CR46 doi: 10.1109/ICASSP.2002.5745451 – volume: 102 start-page: 334 year: 2004 ident: 634_CR18 publication-title: Cancer. doi: 10.1002/cncr.20720 – volume: 7 start-page: e29740 year: 2012 ident: 634_CR34 publication-title: PLoS ONE. doi: 10.1371/journal.pone.0029740 – volume: 16 start-page: 95 year: 2000 ident: 634_CR49 publication-title: Signal Process Image Commun. doi: 10.1016/S0923-5965(00)00019-9 – volume: 113 start-page: 539 issue: 2 year: 2014 ident: 634_CR22 publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2013.12.012 – volume: 20 start-page: 1130 year: 2012 ident: 634_CR54 publication-title: IEEE Trans Fuzzy Syst. doi: 10.1109/TFUZZ.2012.2201485 – ident: 634_CR41 doi: 10.21437/Interspeech.2014-226 – volume: 65 start-page: 87 issue: 2 year: 2015 ident: 634_CR1 publication-title: CA A Cancer J Clin. doi: 10.3322/caac.21262 – volume: 2014 start-page: 842037 year: 2014 ident: 634_CR8 publication-title: Comput Math Methods Med. doi: 10.1155/2014/842037 – ident: 634_CR45 doi: 10.1016/B978-0-12-398358-9.00007-0 – volume: 138 start-page: 31 year: 2017 ident: 634_CR64 publication-title: Comput Methods Programs Biomed. doi: 10.1016/j.cmpb.2016.10.001 – volume: 9 start-page: 449 year: 1987 ident: 634_CR14 publication-title: Anal Quant Cytol Histol. – ident: 634_CR31 – volume: 6 start-page: 497 issue: 6 year: 2004 ident: 634_CR4 publication-title: Curr Oncol Rep. doi: 10.1007/s11912-004-0083-5 – volume: 1 start-page: 200 year: 1980 ident: 634_CR15 publication-title: Cytometry. doi: 10.1002/cyto.990010305 – start-page: 1 volume-title: Recent evidence on cervical cancer screening in low-resource settings year: 2011 ident: 634_CR6 – volume: 111 start-page: 128 year: 2013 ident: 634_CR43 publication-title: Comput Methods Programs Biomed. doi: 10.1016/j.cmpb.2013.02.008 – volume: 89 start-page: 651 year: 2016 ident: 634_CR39 publication-title: Procedia Comput Sci. doi: 10.1016/j.procs.2016.06.029 – volume: 23 start-page: 315 year: 2007 ident: 634_CR36 publication-title: Dis Mark. doi: 10.1155/2007/678793 – ident: 634_CR21 doi: 10.1155/2015/457906 – volume: 3 start-page: 460 issue: 1 year: 2013 ident: 634_CR23 publication-title: Markers. – volume: 5 start-page: 342 year: 2002 ident: 634_CR35 publication-title: Nat Rev Cancer. doi: 10.1038/nrc798 – volume: 16 start-page: 41 issue: 32 year: 2008 ident: 634_CR5 publication-title: Reprod Health Matters. doi: 10.1016/S0968-8080(08)32415-X |
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| Snippet | Background
Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and... Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of... Background Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and... Abstract Background Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening... |
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| SubjectTerms | Accuracy Algorithms Automation Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Biotechnology Cancer Cancer screening Care and treatment Cervical cancer Cervix Classification Classifiers Computational intelligence and data mining in biomedical engineering Computer aided medical diagnosis Computer simulation Cost analysis Diagnosis Early Detection of Cancer Engineering Error analysis Female Fuzzy C-means Fuzzy Logic Fuzzy sets Hand tools Humans Image classification Image detection Image processing Image Processing, Computer-Assisted Image retrieval Image segmentation International conferences Medical imaging Medical screening Methods Pap smear Pap test Papanicolaou Test Pathology Sensitivity Sensitivity and Specificity Simulated annealing Technology application Uterine Cervical Neoplasms - diagnosis Uterine Cervical Neoplasms - diagnostic imaging Workflow |
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| Title | A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images |
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