Enhanced Boykov's graph cuts based segmentation for Cervical Cancer Detection

Balaji, G.; Suryanarayana, S. V.; Sengathir, J.

doi:10.4108/eai.7-7-2021.170284

Cited by 5 publications

(7 citation statements)

References 19 publications

(21 reference statements)

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“…Mustafa et al, [27] reported the sensitivity achieved is about 94.38%, while Lavanya Devi and Thirumurugan [4] achieved 97.96%. The specificity of the nuclei segmentation technique is 82.56% and 83.65% and has been demonstrated in these two reports [25].…”

Section: Review Of the Study 21 Nucleus Segmentationsupporting

confidence: 54%

“…Furthermore, an edge augmentation approach relying on mathematical morphology and the canny operator is provided, which retrieves edge information as a weight to boost the nucleus edge specifically [24]. The Boykov-Kolmogorov Graph Cuts (BKGC) scheme is suggested from a study by Balaji et al, [25]. It presents an efficient BKGC-based image partitioning approach that predicts the image data utilizing a synergy cloud model to form objective functions.…”

Section: Review Of the Study 21 Nucleus Segmentationmentioning

confidence: 99%

“…It presents an efficient BKGC-based image partitioning approach that predicts the image data utilizing a synergy cloud model to form objective functions. Furthermore, in estimating every pixel's deviation on the distinct areas of the cervical Pap smear cells, the suggested BKGC-CMSIS scheme incorporates a data item as a smooth term regarding boundary preservation, as shown in Figure 3 [25].…”

Section: Review Of the Study 21 Nucleus Segmentationmentioning

confidence: 99%

“…3. The generated images following enforcing the suggested BKGC-CMSIS Scheme's sequential phases throughout deployment [25] This image is provided by Shiney and Rose [26], explaining that three innovative approaches for the automated identification and categorization of cervical cancer in Pap smear images have been created. This framework consists of five stages: data collection, pre-processing, segmentation, extraction of features, and image categorization.…”

Section: Review Of the Study 21 Nucleus Segmentationmentioning

confidence: 99%

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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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Section: Review Of the Study 21 Nucleus Segmentationsupporting

confidence: 54%

Section: Review Of the Study 21 Nucleus Segmentationmentioning

confidence: 99%

Section: Review Of the Study 21 Nucleus Segmentationmentioning

confidence: 99%

Section: Review Of the Study 21 Nucleus Segmentationmentioning

confidence: 99%

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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 is a major disease that seriously threatens women’s health [ 1 , 2 ]. This cancer is also known as the second most commonly affected and killer type of cancer among women around the world [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Pap Smear Images Classification Using Machine Learning: A Literature Matrix

et al. 2022

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Cervical cancer is regularly diagnosed in women all over the world. This cancer is the seventh most frequent cancer globally and the fourth most prevalent cancer among women. Automated and higher accuracy of cervical cancer classification methods are needed for the early diagnosis of cancer. In addition, this study has proved that routine Pap smears could enhance clinical outcomes by facilitating the early diagnosis of cervical cancer. Liquid-based cytology (LBC)/Pap smears for advanced cervical screening is a highly effective precancerous cell detection technology based on cell image analysis, where cells are classed as normal or abnormal. Computer-aided systems in medical imaging have benefited greatly from extraordinary developments in artificial intelligence (AI) technology. However, resource and computational cost constraints prevent the widespread use of AI-based automation-assisted cervical cancer screening systems. Hence, this paper reviewed the related studies that have been done by previous researchers related to the automation of cervical cancer classification based on machine learning. The objective of this study is to systematically review and analyses the current research on the classification of the cervical using machine learning. The literature that has been reviewed is indexed by Scopus and Web of Science. As a result, for the published paper access until October 2022, this study assessed past approaches for cervical cell classification based on machine learning applications.

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