A Review on Poly-cystic Ovary Syndrome Risk Evaluation System Using Segmentation in Deep Learning

Author(s)	Shalu Thakur, Dr.Ashwini Jha
Country	India
Abstract	A common endocrine disorder affecting fertile women is PCOS. High testosterone levels, ovarian cysts, oligomenorrhea, and anovulation are its hallmarks. Traditional diagnostic methods, while well established, often lack specificity and provide no personalized information about the disease's progression. Viable alternatives are offered by recent advances in deep learning (DL), which increase diagnostic accuracy by employing large and complex datasets. Using a dataset of 541 patients from Kaggle, the traditional classifiers showed high accuracy, but the deep learning model performed better.
Keywords	PCOS, deep learning, Ultrasound, Segmentation
Field	Computer Applications
Published In	Volume 7, Issue 1, January-February 2025
Published On	2025-01-05
Cite This	A Review on Poly-cystic Ovary Syndrome Risk Evaluation System Using Segmentation in Deep Learning - Shalu Thakur, Dr.Ashwini Jha - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.34268
DOI	https://doi.org/10.36948/ijfmr.2025.v07i01.34268
Short DOI	https://doi.org/g82hjz

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