Detection of Polycystic Ovarian Syndrome: A Literature Survey

Saravanan, A.; Sathiamoorthy, S.

doi:10.51983/ajeat-2018.7.2.1008

Cited by 14 publications

(10 citation statements)

References 38 publications

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“…Reviews and surveys of image segmentation methods have been conducted to determine a superior segmentation technique that is able to detect and calculate the number of follicles in the ovary [2,15,17,23,[28][29][30][31][32][33][34][35]. One study [35] concluded that no exclusive method could be dominant considering the various characteristics of ultrasound images, especially ovarian ultrasound images.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Polycystic ovarian syndrome (PCOS) is an ovarian health condition of endocrine dysfunction that affects the health of women's reproductive systems and is characterized by an ovary with a follicle count ≥12 and a size of 2 or 3 mm 3 [1]. This syndrome produces side effects related to women's overall health, such as abnormal hair growth, weight gain, irregular menstrual cycle, infertility and depression [2,3]. Almost 70% of women with PCOS suffer from infertility [4,5], and this condition is one of the common reasons for female infertility [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring

et al. 2023

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Experts have used ultrasound imaging to manually determine follicle count and perform measurements, especially in cases of polycystic ovary syndrome (PCOS). However, due to the laborious and error-prone process of manual diagnosis, researchers have explored and developed medical image processing techniques to help with diagnosing and monitoring PCOS. This study proposes a combination of Otsu’s thresholding with the Chan–Vese method to segment and identify follicles in the ovary with reference to ultrasound images marked by a medical practitioner. Otsu’s thresholding highlights the pixel intensities of the image and creates a binary mask for use with the Chan–Vese method to define the boundary of the follicles. The acquired results were compared between the classical Chan–Vese method and the proposed method. The performances of the methods were evaluated in terms of accuracy, Dice score, Jaccard index and sensitivity. In overall segmentation evaluation, the proposed method showed superior results compared to the classical Chan–Vese method. Among the calculated evaluation metrics, the sensitivity of the proposed method was superior, with an average of 0.74 ± 0.12. Meanwhile, the average sensitivity for the classical Chan–Vese method was 0.54 ± 0.14, which is 20.03% lower than the sensitivity of the proposed method. Moreover, the proposed method showed significantly improved Dice score (p = 0.011), Jaccard index (p = 0.008) and sensitivity (p = 0.0001). This study showed that the combination of Otsu’s thresholding and the Chan–Vese method enhanced the segmentation of ultrasound images.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring

et al. 2023

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“…Since 2003, most researchers have used the inception of twelve follicles (having 2-9 mm measurement in diameter) per complete ovary. However, that now appears to be outdated [26].…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics

et al. 2022

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Polycystic ovary syndrome (PCOS) is a critical disorder in women during their reproduction phase. The PCOS disorder is commonly caused by excess male hormone and androgen levels. The follicles are the collections of fluid developed by ovaries and may fail to release eggs regularly. The PCOS results in miscarriage, infertility issues, and complications during pregnancy. According to a recent report, PCOS is diagnosed in 31.3% of women from Asia. Studies show that 69% to 70% of women did not avail of a detecting cure for PCOS. A research study is needed to save women from critical complications by identifying PCOS early. The main aim of our research is to predict PCOS using advanced machine learning techniques. The dataset based on clinical and physical parameters of women is utilized for building study models. A novel feature selection approach is proposed based on the optimized chi-squared (CS-PCOS) mechanism. The ten hyper-parametrized machine learning models are applied in comparison. Using the novel CS-PCOS approach, the gaussian naive bayes (GNB) outperformed machine learning models and state-of-the-art studies. The GNB achieved 100% accuracy, precision, recall, and f1-scores with minimal time computations of 0.002 seconds. The k-fold cross-validation of GNB achieved a 100% accuracy score. The proposed GNB model achieved accurate results for critical PCOS prediction. Our study reveals that the dataset features prolactin (PRL), blood pressure systolic, blood pressure diastolic, thyroid stimulating hormone (TSH), relative risk (RR-breaths), and pregnancy are the prominent factors having high involvement in PCOS prediction. Our research study helps the medical community overcome the miscarriage rate and provide a cure to women through the early detection of PCOS.

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“…Follicles, or cysts, which are fluid-filled sacs, are found on the ovary's periphery. A polycystic ovary (PCO) is defined as having twelve or more follicles that are between two to nine mm in diameter [3] . Both health and the standard of living for women are impacted by PCOS.…”

Section: Introductionmentioning

confidence: 99%

“…According to reports, the prevalence ranges from 2.2–26% globally [6] . According to community research conducted in the United Kingdom (UK), South Asians exhibit a prevalence of 52% when compared to Caucasians, who exhibit a prevalence of 22% [3] . Early identification and treatment can be used to control the symptoms and avoid long-term problems.…”

Section: Introductionmentioning

confidence: 99%

Mathematical study of polycystic ovarian syndrome disease including medication treatment mechanism for infertility in women

Batool,

Farman,

Ahmad

et al. 2024

AIMSPH

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<abstract> <p>Among women of reproductive age, PCOS (polycystic ovarian syndrome) is one of the most prevalent endocrine illnesses. In addition to decreasing female fertility, this condition raises the risk of cardiovascular disease, diabetes, dyslipidemia, obesity, psychiatric disorders and other illnesses. In this paper, we constructed a fractional order model for polycystic ovarian syndrome by using a novel approach with the memory effect of a fractional operator. The study population was divided into four groups for this reason: Women who are at risk for infertility, PCOS sufferers, infertile women receiving therapy (gonadotropin and clomiphene citrate), and improved infertile women. We derived the basic reproductive number, and by utilizing the Jacobian matrix and the Routh-Hurwitz stability criterion, it can be shown that the free and endemic equilibrium points are both locally stable. Using a two-step Lagrange polynomial, solutions were generated in the generalized form of the power law kernel in order to explore the influence of the fractional operator with numerical simulations, which shows the impact of the sickness on women due to the effect of different parameters involved.</p> </abstract>

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Detection of Polycystic Ovarian Syndrome: A Literature Survey

Cited by 14 publications

References 38 publications

Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring

Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring

A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics

Mathematical study of polycystic ovarian syndrome disease including medication treatment mechanism for infertility in women

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