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. 2019 Feb 12;18(1):16.
doi: 10.1186/s12938-019-0634-5.

A Pap-Smear Analysis Tool (PAT) for Detection of Cervical Cancer From Pap-Smear Images

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Free PMC article

A Pap-Smear Analysis Tool (PAT) for Detection of Cervical Cancer From Pap-Smear Images

Wasswa William et al. Biomed Eng Online. .
Free PMC article

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.

Keywords: Cervical cancer; Fuzzy C-means; Pap-smear.

Figures

Fig. 1
Fig. 1
The approach to achieve cervical cancer detection from pap-smear images
Fig. 2
Fig. 2
Generation of the feature vector from the training images
Fig. 3
Fig. 3
Three-phase sequential elimination approach for debris rejection
Fig. 4
Fig. 4
The fuzzy C-means is wrapped into a black box from which an estimated error is obtained
Fig. 5
Fig. 5
PAT graphical user interface
Fig. 6
Fig. 6
Boxplot for the percentage error in the extracted features
Fig. 7
Fig. 7
Boxplot for the percentage error in the extracted features from 50 normal pap-smear slides (first boxplot) and 50 abnormal pap-smear slides (second boxplot)
Fig. 8
Fig. 8
ROC curve for the classifier performance on Dataset 1

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