<?xml version='1.0' encoding='UTF-8'?><codeBook xmlns="ddi:codebook:2_5" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="ddi:codebook:2_5 https://ddialliance.org/Specification/DDI-Codebook/2.5/XMLSchema/codebook.xsd" version="2.5"><docDscr><citation><titlStmt><titl>Polycystic Ovary Ultrasound Images Dataset</titl><IDNo agency="DOI">doi:10.34820/FK2/QVCP6V</IDNo></titlStmt><distStmt><distrbtr source="archive">Root</distrbtr><distDate>2021-03-04</distDate></distStmt><verStmt source="archive"><version date="2021-03-04" type="RELEASED">1</version></verStmt><biblCit>Adiwijaya; NOVIA WISESTY, UNTARI; Widi Astuti, 2021, "Polycystic Ovary Ultrasound Images Dataset", https://doi.org/10.34820/FK2/QVCP6V, Root, V1</biblCit></citation></docDscr><stdyDscr><citation><titlStmt><titl>Polycystic Ovary Ultrasound Images Dataset</titl><IDNo agency="DOI">doi:10.34820/FK2/QVCP6V</IDNo></titlStmt><rspStmt><AuthEnty affiliation="Telkom University">Adiwijaya</AuthEnty><AuthEnty affiliation="Telkom University">NOVIA WISESTY, UNTARI</AuthEnty><AuthEnty affiliation="Telkom University">Widi Astuti</AuthEnty></rspStmt><prodStmt/><distStmt><distrbtr source="archive">Root</distrbtr><contact affiliation="Telkom University" email="untarinw@telkomuniversity.ac.id">NOVIA WISESTY, UNTARI</contact><depositr>NOVIA WISESTY, UNTARI</depositr><depDate>2021-03-04</depDate></distStmt></citation><stdyInfo><subject><keyword>Computer and Information Science</keyword><keyword>Medicine, Health and Life Sciences</keyword><keyword>Polycystic Ovary (PCO)</keyword><keyword>Polycystic Ovary Syndrome (PCOS)</keyword><keyword>Ultrasound Images</keyword></subject><abstract date="2021-03-04">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.</abstract><sumDscr/></stdyInfo><method><dataColl><sources/></dataColl><anlyInfo/></method><dataAccs><notes type="DVN:TOU" level="dv">CC0 Waiver</notes><setAvail/><useStmt/></dataAccs><othrStdyMat><relPubl><citation><titlStmt><IDNo agency="doi">https://doi.org/10.3844/jcssp.2018.351.359</IDNo></titlStmt><biblCit>Study of Segmentation Technique and Stereology to Detect PCO Follicles on USG Images</biblCit></citation><ExtLink URI="https://www.thescipub.com/abstract/jcssp.2018.351.359"/></relPubl><relPubl><citation><titlStmt><IDNo agency="doi">https://doi.org/10.1088/1742-6596/622/1/012027</IDNo></titlStmt><biblCit>Follicle Detection on the USG Images to Support Determination Polycystic Ovary Syndrome</biblCit></citation><ExtLink URI="https://iopscience.iop.org/article/10.1088/1742-6596/622/1/012027/meta"/></relPubl><relPubl><citation><titlStmt><IDNo agency="doi">https://doi.org/10.1007/978-3-319-51281-5_15</IDNo></titlStmt><biblCit>Modified Backpropagation Algorithm for Polycystic Ovary Syndrome Detection Based on Ultrasound Images</biblCit></citation><ExtLink URI="https://link.springer.com/chapter/10.1007/978-3-319-51281-5_15"/></relPubl><relPubl><citation><titlStmt><IDNo agency="doi">https://doi.org/10.1088/1742-6596/971/1/012016</IDNo></titlStmt><biblCit>An Implementation of Elman Neural Network for Polycystic Ovary Classification based on Ultrasound Images</biblCit></citation><ExtLink URI="https://iopscience.iop.org/article/10.1088/1742-6596/971/1/012016"/></relPubl><relPubl><citation><titlStmt><IDNo agency="doi">https://doi.org/10.1088/1742-6596/971/1/012005</IDNo></titlStmt><biblCit>Classification of Polycystic Ovary based on Ultrasound Images using Competitive Neural Network</biblCit></citation><ExtLink URI="https://iopscience.iop.org/article/10.1088/1742-6596/971/1/012005"/></relPubl><relPubl><citation><titlStmt><IDNo agency="doi">10.1109/ICoICT.2015.7231458</IDNo></titlStmt><biblCit>A classification of polycystic Ovary Syndrome based on follicle detection of ultrasound images</biblCit></citation></relPubl><relPubl><citation><titlStmt><IDNo agency="doi">10.1109/ICoICT.2015.7231453</IDNo></titlStmt><biblCit>Particle swarm optimization on follicles segmentation to support PCOS detection</biblCit></citation></relPubl></othrStdyMat></stdyDscr><otherMat ID="f66" URI="https://doi.org/10.34820/FK2/QVCP6V/CSOKHK" level="datafile"><labl>normal_10.jpg</labl><notes level="file" type="DATAVERSE:CONTENTTYPE" subject="Content/MIME Type">image/jpeg</notes></otherMat><otherMat ID="f72" URI="https://doi.org/10.34820/FK2/QVCP6V/GZ0XQF" level="datafile"><labl>normal_11.jpg</labl><notes level="file" type="DATAVERSE:CONTENTTYPE" subject="Content/MIME Type">image/jpeg</notes></otherMat><otherMat ID="f110" URI="https://doi.org/10.34820/FK2/QVCP6V/IL3DLV" level="datafile"><labl>normal_12.jpg</labl><notes level="file" 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