A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images

Abstract

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.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.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.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.

Point-of-Care Digital Cytology With Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting

“…Notably, however, none of the slides that were classified as negative by the DLS were classified as atypical in the cytodiagnosis of the physical slides. Previous studies have reported encouraging results with the deep learning–based analysis of smaller cropped images from Papanicolaou smears 26 , 27 , 29 , 39 that were digitized with conventional slide scanners, but clinical application requires the examination of substantially larger sample areas. 28 In this study, we used routine samples collected at the clinic, and correspondingly, the whole-slide images were magnitudes larger than those previously analyzed, measuring on average 100 387 × 47 560 pixels; thus, the total number of pixels analyzed corresponded to approximately twice the number in the entire ImageNet database (>14 million images of everyday objects) at commonly used resolutions.…”

Automatic model for cervical cancer screening based on convolutional neural network: a retrospective, multicohort, multicenter study

“…This is mainly because TCT images are relatively difficult to analyze compared to the pap smear images. More specifically: (1) the TCT images do not currently have a good database, and the collection of such an image database is more difficult compared to the Harlve database of pap-smear images [ 38 , 39 ]; (2) TCT images have many overlapping cells, which are not as easy to analyze as the single cells in pap-smear images [ 40 , 41 ]; (3) the color and quality of TCT images obtained from different medical institutions may vary greatly. Therefore, these systems are poorly adapted for the proposed system, we trained it with data from hospitals at different levels in different regions of north and south China, which ensured the high adaptability of the system.…”

Point-of-care digital cytology with artificial intelligence for cervical cancer screening at a peripheral clinic in Kenya

“…Previous studies have reported encouraging results with the deep learning-based analysis of smaller cropped images from Pap smears 26,27,29,39 that were digitized with conventional slide scanners, but clinical application requires the examination of substantially larger sample areas. 28 In this study, we used routine samples collected in the clinic, and correspondingly, the whole-slide images were magnitudes larger than those previously analysed, measuring on average 100,387 × 47,560 pixels; thus, the total number of pixels analysed corresponded to approximately twice the number in the entire ImageNet database at commonly used resolutions.…”