Three segmentation techniques to predict the dysplasia in cervical cells in the presence of debris

Arya, Mithlesh; Mittal, Namita; Singh, Girdhari

doi:10.1007/s11042-020-09206-9

Cited by 6 publications

(9 citation statements)

References 23 publications

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“…One of the most broadly utilized performance analysis methods is qualitative analysis. Probabilistic statements about the algorithm's performance and weaknesses are based on human visual perception [40,54].…”

Section: Qualitative Resultsmentioning

confidence: 99%

“…In a study by Arya et al [40], the first step in analyzing the results of three segmentation techniques to predict the dysplastic in cervical cells in the presence of debris is extracting the Region of Interest (ROI). Normal cell nuclei, abnormal cell nuclei, and debris are detected in ROI, and the area of all the objects is computed.…”

Section: Qualitative Resultsmentioning

confidence: 99%

“…Some debris has a nucleus that matches regular cells, causing interference and false-positive results. Research demonstrates that Modified Moving k-means are more accurate in identifying dysplastic in the presence of debris [40].…”

Section: Detection Methods Based On Cells/pap Smear Imagesmentioning

confidence: 95%

“…The findings of the study show that this ensemble method achieved the best result concerning the F1 and recall values. With the same objective, three segmentation strategies for automated segmentation of cervical cell nuclei in the presence of debris are described by Arya et al [40]. Automated Seed Region Growing, Extended Edge Based Detection, and Modified Moving Segmentation is three segmentation approaches.…”

Section: Detection Methods Based On Cells/pap Smear Imagesmentioning

confidence: 99%

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Improvement method for cervical cancer detection: A comparative analysis

Alias¹,

Mustafa²,

Jamlos³

et al. 2021

Oncology Research

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Cervical cancer is a prevalent and deadly cancer that affects women all over the world. It affects about 0.5 million women anually and results in over 0.3 million fatalities. Diagnosis of this cancer was previously done manually, which could result in false positives or negatives. The researchers are still contemplating how to detect cervical cancer automatically and how to evaluate Pap smear images. Hence, this paper has reviewed several detection methods from the previous researches that has been done before. This paper reviews pre-processing, detection method framework for nucleus detection, and analysis performance of the method selected. There are four methods based on a reviewed technique from previous studies that have been running through the experimental procedure using Matlab, and the dataset used is established Herlev Dataset. The results show that the highest performance assessment metric values obtain from Method 1: Thresholding and Trace region boundaries in a binary image with the values of precision 1.0, sensitivity 98.77%, specificity 98.76%, accuracy 98.77% and PSNR 25.74% for a single type of cell. Meanwhile, the average values of precision were 0.99, sensitivity 90.71%, specificity 96.55%, accuracy 92.91% and PSNR 16.22%. The experimental results are then compared to the existing methods from previous studies. They show that the improvement method is able to detect the nucleus of the cell with higher performance assessment values. On the other hand, the majority of current approaches can be used with either a single or a large number of cervical cancer smear images. This study might persuade other researchers to recognize the value of some of the existing detection techniques and offer a strong approach for developing and implementing new solutions.

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Section: Qualitative Resultsmentioning

confidence: 99%

Section: Qualitative Resultsmentioning

confidence: 99%

Section: Detection Methods Based On Cells/pap Smear Imagesmentioning

confidence: 95%

Section: Detection Methods Based On Cells/pap Smear Imagesmentioning

confidence: 99%

See 2 more Smart Citations

Improvement method for cervical cancer detection: A comparative analysis

Alias¹,

Mustafa²,

Jamlos³

et al. 2021

Oncology Research

View full text Add to dashboard Cite

show abstract

“…The calculated values can be calculated based on the segmented images. These findings demonstrate the importance of validating image quality using the suggested techniques on the Pap smear dataset [39]. The process is evaluated subjectively by comparing the segmented pictures pixel by pixel with the ground truth images, and quantitatively by counting the number of cells (objects) in the images [40].…”

Section: Tablementioning

confidence: 86%

Edge Enhancement and Detection Approach on Cervical Cytology Images

Alias

Mustafa

Jamlos

et al. 2022

ARASET

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Cervical cancer is a prevalent and fatal disease that affects women all over the world. This affects roughly 0.5 million women annually and kills over 0.3 million people. Recently, a significant amount of literature has emerged around the advancement of technologies for identifying cervical cancer cells in women. Previously, diagnosing cervical cancer was done manually, which could lead to false positives or negatives. The best way of interpreting Pap smear images and automatically diagnose cervical cancer are still up for debate among the researchers. Method used in this study is the contrast enhancement technique for pre-processing and edge detection-based for segmentation of the nucleus. In this study, the average performance results of the method showed an accuracy of 96.99% in the seven-class problem using Herlev dataset. The present finding also support this study which concluded the results of accuracy achieved for the algorithm used for nucleus detection is improved by 6.15% when comparing to previous work. The accuracy value is in the lines of earlier literature that achieved accuracy of the approach used above 90% for seven class of cells. The major feature of the suggested approach is an improvement in the ability to anticipate which cells are aberrant and which are normal. Adding more classifiers could improve the suggested system even further. Therefore, a cervical cancer screening system might utilize this framework to identify women who have precancerous lesions.

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