Coloring of Cervical Cancer’s Ct Images to Localize Cervical Cancer

Putri, Erlinda Ratnasari; Nasrulloh, Amar Vijai; Fahrudin, Arfan Eko

doi:10.11591/ijece.v5i2.pp304-310

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…By searching Google Scholar, PubMed, and Web of Science websites, only four articles met the requirements. Putri et al ( 13 ) used the fuzzy C-mean method to achieve segmentation of the cervical region and localization of cervical cancer in 2015. Other three studies utilized atlas for their segmentation.…”

Section: Current State Of Cervical Cancer Segmentation Methods For Di...mentioning

confidence: 99%

Cervical cancer segmentation based on medical images: a literature review

Wang,

Feng,

Huang

et al. 2024

Quant Imaging Med Surg

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Background and Objective Cervical cancer clinical target volume (CTV) outlining and organs at risk segmentation are crucial steps in the diagnosis and treatment of cervical cancer. Manual segmentation is inefficient and subjective, leading to the development of automated or semi-automated methods. However, limitation of image quality, organ motion, and individual differences still pose significant challenges. Apart from numbers of studies on the medical images’ segmentation, a comprehensive review within the field is lacking. The purpose of this paper is to comprehensively review the literatures on different types of medical image segmentation regarding cervical cancer and discuss the current level and challenges in segmentation process. Methods As of May 31, 2023, we conducted a comprehensive literature search on Google Scholar, PubMed, and Web of Science using the following term combinations: “cervical cancer images”, “segmentation”, and “outline”. The included studies focused on the segmentation of cervical cancer utilizing computed tomography (CT), magnetic resonance (MR), and positron emission tomography (PET) images, with screening for eligibility by two independent investigators. Key Content and Findings This paper reviews representative papers on CTV and organs at risk segmentation in cervical cancer and classifies the methods into three categories based on image modalities. The traditional or deep learning methods are comprehensively described. The similarities and differences of related methods are analyzed, and their advantages and limitations are discussed in-depth. We have also included experimental results by using our private datasets to verify the performance of selected methods. The results indicate that the residual module and squeeze-and-excitation blocks module can significantly improve the performance of the model. Additionally, the segmentation method based on improved level set demonstrates better segmentation accuracy than other methods. Conclusions The paper provides valuable insights into the current state-of-the-art in cervical cancer CTV outlining and organs at risk segmentation, highlighting areas for future research.

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Section: Current State Of Cervical Cancer Segmentation Methods For Di...mentioning

confidence: 99%

Cervical cancer segmentation based on medical images: a literature review

Wang,

Feng,

Huang

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

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“…Among the disadvantages of cervical cancer is the inability to detect it early, which makes it difficult to treat. Another disadvantage is that it infects young women [7]. Fig.…”

Section: Introductionmentioning

confidence: 99%

ReliefF and Association Rule Mining to Determine Cervical Cancer Causes

weli

2021

J. Al-Qadisiyah Comp. Sci. Math.

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Cancer patients till this day suffer from the inability of science to predict the causes of the disease before it occurs. One of the cancers that occupy the minds of many women is the cervical cancer because of the delay in its diagnosis as a result of its multiple and unclear causes, so scientists and researchers need to search for the most causative factors. Machine learning approaches have become one of the best and fastest ways to find associations between symptoms and causes of disease. The use of association rule mining (AR) is very effective if diagnostic features are set up. In this work, feature selection (FS) algorithm named ReliefF is used to reach the most correlated factor, then the Apriori algorithm has been updated to reduce the time and space used, and detects features that are closely related to the class attribute to access most factors that cause cervical cancer. The experimental results of the proposed work indicate a number of cervical cancer risk factors that when combined, indicate a woman's likelihood of developing cervical cancer, which is: the number of years of hormonal contraception is greater than or equal to 15, having any type of cancer or HPV or syphilis or HIV, the number of IUD insertion years exceeded 10, First sexual intercourse smaller than 13 and Number of sexual partners greater than 5. The outcomes of this work help both doctors and women to prevent cancer.

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Coloring of Cervical Cancer’s Ct Images to Localize Cervical Cancer

Cited by 2 publications

References 17 publications

Cervical cancer segmentation based on medical images: a literature review

Cervical cancer segmentation based on medical images: a literature review

ReliefF and Association Rule Mining to Determine Cervical Cancer Causes

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