dc.contributor.author | WILLIAM, Wasswa | |
dc.contributor.author | WARE, Andrew | |
dc.contributor.author | BASAZA-EJIRI, Annabella Habinka | |
dc.contributor.author | OBUNGOLOCH, Johnes | |
dc.date.accessioned | 2022-05-09T17:25:11Z | |
dc.date.available | 2022-05-09T17:25:11Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | WILLIAM, W., Andrew, W. A. R. E., BASAZA-EJIRI, A. H., & OBUNGOLOCH, J. (2019, May). Automated diagnosis and classification of cervical cancer from pap-smear images. In 2019 IST-Africa Week Conference (IST-Africa) (pp. 1-11). IEEE. | en_US |
dc.identifier.uri | http://ir.must.ac.ug/xmlui/handle/123456789/1879 | |
dc.description.abstract | Globally, cervical cancer ranks as the fourth most prevalent cancer affecting women. However, cervical cancer can be treated if detected at an early stage. Pap-smear is a good tool for screening of cervical cancer but the manual analysis is error-prone, tedious and time-consuming. The objective of this study was to rule out these limitations by automating the process of cervical cancer classification from pap-smear images by using an enhanced fuzzy c-means algorithm. Simulated annealing coupled with a wrapper filter was used for feature selection. The evaluation results showed that our method outperforms many of previous algorithms in classification accuracy (99.35%), specificity (97.93%) and sensitivity (99.85%), when applied to the Herlev benchmark pap-smear dataset. The overall accuracy, sensitivity and specificity of the classifier on prepared pap-smear slides was 95.00%, 100% and 90.00% respectively. False Negative Rate (FNR), False Positive Rate (FPR) and classification error of 0.00%, 10.00% and 5.00% respectively were obtained. 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 tool has the capability of analyzing 1-2 smears per minute as opposed to the 5-10 minutes per slide in the manual analysis. | en_US |
dc.description.sponsorship | African Development Bank- HEST project | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Pap-smear | en_US |
dc.subject | Cervical Cancer | en_US |
dc.subject | Automated Analysis | en_US |
dc.title | Automated Diagnosis and Classification of Cervical Cancer from pap-smear Images | en_US |
dc.type | Article | en_US |