<?xml version='1.0' encoding='UTF-8'?><metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns="http://dublincore.org/documents/dcmi-terms/"><dcterms:title>Polycystic Ovary Ultrasound Images Dataset</dcterms:title><dcterms:identifier>https://doi.org/10.34820/FK2/QVCP6V</dcterms:identifier><dcterms:creator>Adiwijaya</dcterms:creator><dcterms:creator>NOVIA WISESTY, UNTARI</dcterms:creator><dcterms:creator>Widi Astuti</dcterms:creator><dcterms:publisher>Root</dcterms:publisher><dcterms:issued>2021-03-04</dcterms:issued><dcterms:modified>2021-03-04T07:39:22Z</dcterms:modified><dcterms:description>This dataset contains ultrasound images of patients suffering from Polycystic Ovary Syndrome (PCOS) and normal patients. Each image in the dataset has been categorized into PCOS and Normal classes, which are annotations from specialist doctors.</dcterms:description><dcterms:subject>Computer and Information Science</dcterms:subject><dcterms:subject>Medicine, Health and Life Sciences</dcterms:subject><dcterms:subject>Polycystic Ovary (PCO)</dcterms:subject><dcterms:subject>Polycystic Ovary Syndrome (PCOS)</dcterms:subject><dcterms:subject>Ultrasound Images</dcterms:subject><dcterms:isReferencedBy>Study of Segmentation Technique and Stereology to Detect PCO Follicles on USG Images, doi, https://doi.org/10.3844/jcssp.2018.351.359, https://www.thescipub.com/abstract/jcssp.2018.351.359</dcterms:isReferencedBy><dcterms:isReferencedBy>Follicle Detection on the USG Images to Support Determination Polycystic Ovary Syndrome, doi, https://doi.org/10.1088/1742-6596/622/1/012027, https://iopscience.iop.org/article/10.1088/1742-6596/622/1/012027/meta</dcterms:isReferencedBy><dcterms:isReferencedBy>Modified Backpropagation Algorithm for Polycystic Ovary Syndrome Detection Based on Ultrasound Images, doi, https://doi.org/10.1007/978-3-319-51281-5_15, https://link.springer.com/chapter/10.1007/978-3-319-51281-5_15</dcterms:isReferencedBy><dcterms:isReferencedBy>An Implementation of Elman Neural Network for Polycystic Ovary Classification based on Ultrasound Images, doi, https://doi.org/10.1088/1742-6596/971/1/012016, https://iopscience.iop.org/article/10.1088/1742-6596/971/1/012016</dcterms:isReferencedBy><dcterms:isReferencedBy>Classification of Polycystic Ovary based on Ultrasound Images using Competitive Neural Network, doi, https://doi.org/10.1088/1742-6596/971/1/012005, https://iopscience.iop.org/article/10.1088/1742-6596/971/1/012005</dcterms:isReferencedBy><dcterms:isReferencedBy>A classification of polycystic Ovary Syndrome based on follicle detection of ultrasound images, doi, 10.1109/ICoICT.2015.7231458</dcterms:isReferencedBy><dcterms:isReferencedBy>Particle swarm optimization on follicles segmentation to support PCOS detection, doi, 10.1109/ICoICT.2015.7231453</dcterms:isReferencedBy><dcterms:contributor>NOVIA WISESTY, UNTARI</dcterms:contributor><dcterms:dateSubmitted>2021-03-04</dcterms:dateSubmitted><dcterms:license>CC0</dcterms:license><dcterms:rights>CC0 Waiver</dcterms:rights></metadata>