Cervical Cancer Classification from Pap Smear Images Using Modified Fuzzy C Means, PCA, and KNN

Devi, N. Lavanya; Thirumurugan, P.

doi:10.1080/03772063.2021.1997353

Cited by 20 publications

(13 citation statements)

References 21 publications

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“…The highest values for this method are 1.00, 98.77, 98.76, 98.77 and 25.74 for precision, sensitivity, specificity, accuracy and PSNR values, respectively. Based on the previous studies that has been reviewed, the performance of approaches used previously has resulted in precision values of 0.98 [46], 0.96 [47], 0.99 [48], sensitivity 98.83% [46], 95.83% [33], 97.96% [47], specificity 97.9% [45], 98.5% [46], 90.62% [33], 83.65% [47], 99.70 [48], accuracy 98.3% [45], 98.38 [46], 95.08% [33], 96.28% [47], 99.70% [48], 90% [49]. The values obtained from the experiment for method 1, which is thresholding and trace region boundaries in a binary image, outperform other methods in terms of precision, sensitivity and specificity from previous studies.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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

“…K-Nearest Neighbour (KNN) classification uses k-fold cross-validation to classify Pap smear images into abnormal and normal cells. The results of the suggested technique are contrasted with Linear Discriminant, Ensemble Bagged trees, and Fine Gaussian SVM [4].…”

Section: Review Of the Study 21 Nucleus Segmentationmentioning

confidence: 99%

“…Cervical cancer has the second-highest death rate among women in developing nations, behind breast cancer, and is among the tumors that can be treated if detected soon enough [4,5]. Early detection and Pap smear cell images' classification are crucial in identifying cervical cancer [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Pap Smear Image Analysis Based on Nucleus Segmentation and Deep Learning – A Recent Review

Alias

Mustafa

Jamlos

et al. 2023

ARASET

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Cervical cancer refers to a dangerous and common illness that impacts women worldwide. Moreover, this cancer affects over 300,000 people each year, with one woman diagnosed every minute. It affects over 0.5 million women annually, leading to over 0.3 million deaths. Recently, considerable literature has grown around developing technologies to detect cervical cancer cells in women. Previously, a cervical cancer diagnosis was made manually, which may result in a false positive or negative. Automated detection of cervical cancer and analysis method of the Papanicolaou (Pap) smear images are still debated among researchers. Thus, this paper reviewed several studies related to the detection method of Pap smear images focusing on Nuclei Segmentation and Deep Learning (DL) from the publication year of 2020, 2021, and 2022. Training, validation, and testing stages have all been the subject of study. However, there are still inadequacies in the current methodologies that have caused limitations to the proposed approaches by researchers. This study may inspire other researchers to view the proposed methods' potential and provide a decent foundation for developing and implementing new solutions.

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“…The practicality of using the texture attribute is its major draw. Support vector machine (SVM), LDA (Linear Discriminant Analysis), k-nearest neighbour (KNN), and ANN (Artificial Neural Networks) are the most frequently utilised classifiers in multi-cell cervical image analysis [21][22][23][24][25][26]. Numerous research studies have been conducted on the detection of cervical cancer, however the majority of these studies only focused on the segmentation of nuclei regions [27].…”

Section: Introductionmentioning

confidence: 99%

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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Cervical Cancer Classification from Pap Smear Images Using Modified Fuzzy C Means, PCA, and KNN

Cited by 20 publications

References 21 publications

Improvement method for cervical cancer detection: A comparative analysis

Improvement method for cervical cancer detection: A comparative analysis

Pap Smear Image Analysis Based on Nucleus Segmentation and Deep Learning – A Recent Review

Edge Enhancement and Detection Approach on Cervical Cytology Images

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