{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:05:12Z","timestamp":1787025912360,"version":"build-2736575974"},"reference-count":30,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technology and Health Care"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>Polycystic Ovary Syndrome (PCOS) is a medical condition that causes hormonal disorders in women in their childbearing years. The hormonal imbalance leads to a delayed or even absent menstrual cycle. Women with PCOS mainly suffer from extreme weight gain, facial hair growth, acne, hair loss, skin darkening, and irregular periods, leading to infertility in rare cases. Doctors usually examine ultrasound images and conclude the affected ovary but are incapable of deciding whether it is a normal cyst, PCOS, or cancer cyst manually.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>OBJECTIVE:<\/jats:title>\n                    <jats:p>To have access to the high-risk crucial PCOS and to detect the condition and the treatment aimed at mitigating health hazards such as endometrial hyperplasia\/cancer, infertility, pregnancy complications, and the long-term burden of chronic diseases such as cardiometabolic disorders linked with PCOS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>The proposed Self-Defined Convolution Neural Network method (SD_CNN) is used to extract the features and machine learning models such as SVM, Random Forest, and Logistic Regression are used to classify PCOS images. The parameter tuning is done with lesser parameters in order to overcome over-fitting issues. The self-defined model predicts the occurrence of the cyst based on the analyzed features and classifies the class labels effectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>The Random Forest Classifier was found to be the most reliable and accurate among Support Vector Machine (SVM) and Logistic Regression (LR), with accuracy being 96.43%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION:<\/jats:title>\n                    <jats:p>The proposed model establishes better trade-off compared to various other approaches and works effectually for PCOS prediction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3233\/thc-230935","type":"journal-article","created":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T11:02:33Z","timestamp":1724151753000},"page":"2893-2909","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["Modelling a self-defined CNN for effectual classification of PCOS from ultrasound images"],"prefix":"10.1177","volume":"32","author":[{"given":"Gokila Brindha","family":"Paramasivam","sequence":"first","affiliation":[{"name":"Kongu Engineering College","place":["India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajalaxmi","family":"Ramasamy Rajammal","sequence":"additional","affiliation":[{"name":"Kongu Engineering College","place":["India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,9]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"crossref","unstructured":"SrivastavaS KumarP ChaudhryV SinghA. Detection of Ovarian Cyst in Ultrasound Images using Fine-Tuned VGG-16 Deep Learning Network. Springer SN Computer Science. 2020; 1(2).","DOI":"10.1007\/s42979-020-0109-6"},{"issue":"1","key":"e_1_3_4_3_2","article-title":"PCOS(Polycystic Ovarian Syndrome) Detection using Deep Learning","volume":"4","author":"Bhosale S","year":"2022","unstructured":"BhosaleS JoshiL ShivsaranA. PCOS(Polycystic Ovarian Syndrome) Detection using Deep Learning. International Research Journal of Modernization in Engineering Technology and Science. 2022; 4(1).","journal-title":"International Research Journal of Modernization in Engineering Technology and Science."},{"issue":"6","key":"e_1_3_4_4_2","article-title":"Detection of Polycystic Ovarian Syndrome using Convolution Neural Networks","volume":"13","author":"Vikas B","year":"2021","unstructured":"VikasB RadhikaY VineeshaK. Detection of Polycystic Ovarian Syndrome using Convolution Neural Networks. International Journal of Current Research and Review. 2021; 13(6).","journal-title":"International Journal of Current Research and Review."},{"key":"e_1_3_4_5_2","unstructured":"KumariS. Classification of PCOS\/PCOD Using Transfer Learning and GAN Architectures to Generate Pseudo Ultrasound Images. 2021."},{"key":"e_1_3_4_6_2","doi-asserted-by":"crossref","unstructured":"SumathiM ChitraP Sakthi PrabhaR SrilathaK. Study and Detection of PCOS related using CNN. IOP Conf. Series: Materials Science and Engineering. 2021; 1070.","DOI":"10.1088\/1757-899X\/1070\/1\/012062"},{"issue":"3","key":"e_1_3_4_7_2","article-title":"Image Segmentation for detecting Polycystic Ovarian Disease using Deep Neural Networks","volume":"7","author":"Sheveta Vashisht P","year":"2021","unstructured":"Sheveta VashishtP. Image Segmentation for detecting Polycystic Ovarian Disease using Deep Neural Networks. International Journal of Computer Sciences and Engineering. 2021; 7(3).","journal-title":"International Journal of Computer Sciences and Engineering."},{"key":"e_1_3_4_8_2","doi-asserted-by":"crossref","unstructured":"Dharmender KumarP. Feature Extraction and Selection of Kidney Ultrasound Images using GLCM and PCA. ScienceDirect Procedia Computer Science. 2020; 167: 1722-1731.","DOI":"10.1016\/j.procs.2020.03.382"},{"issue":"4","key":"e_1_3_4_9_2","article-title":"Multi-level thresholding based follicle detection and classification of polycystic ovary syndrome from the ultrasound images using machine learning","volume":"12","author":"Gopalakrishnan C","year":"2021","unstructured":"GopalakrishnanC IyapparajaM. Multi-level thresholding based follicle detection and classification of polycystic ovary syndrome from the ultrasound images using machine learning. Springer International Journal of System Assurance Engineering and Management. 2021; 12(4).","journal-title":"Springer International Journal of System Assurance Engineering and Management."},{"issue":"6","key":"e_1_3_4_10_2","first-page":"305","article-title":"Feature Extraction in Medical Images by using Deep Learning Approach","volume":"120","author":"Dara S","year":"2018","unstructured":"DaraS TummaP EluriNR KancharlaGR. Feature Extraction in Medical Images by using Deep Learning Approach. International Journal of Pure and Applied Mathematics. 2018; 120(6): 305-312.","journal-title":"International Journal of Pure and Applied Mathematics."},{"issue":"1","key":"e_1_3_4_11_2","article-title":"An Efficient machine learning-based COVID-19 identification utilizing chest X-ray images","volume":"11","author":"Masadeh M","year":"2022","unstructured":"MasadehM MasadehA AlshormanO KhasawnehFH MasadehMA. An Efficient machine learning-based COVID-19 identification utilizing chest X-ray images. International Journal of Artificial Intelligence. 2022; 11(1).","journal-title":"International Journal of Artificial Intelligence."},{"issue":"7","key":"e_1_3_4_12_2","first-page":"1062","article-title":"Follicles Classification to Detect Polycystic Ovary Syndrome Using Glcm and Novel Hybrid Machine Learning","volume":"12","author":"Nilofer NS","year":"2021","unstructured":"NiloferNS RamkumarR. Follicles Classification to Detect Polycystic Ovary Syndrome Using Glcm and Novel Hybrid Machine Learning. Turkish Journal of Computer and Mathematics Education. 2021; 12(7): 1062-1073.","journal-title":"Turkish Journal of Computer and Mathematics Education."},{"key":"e_1_3_4_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksus.2020.04.005"},{"key":"e_1_3_4_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104356"},{"key":"e_1_3_4_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.08.015"},{"key":"e_1_3_4_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02199-1"},{"key":"e_1_3_4_17_2","doi-asserted-by":"crossref","unstructured":"ZhangJ ZhouH NiuY LvJC ChenJ ChengY. CNN and multi-feature extraction based denoising of CT images. Biomedical Signal Processing and Control. 2021; 67.","DOI":"10.1016\/j.bspc.2021.102545"},{"key":"e_1_3_4_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksus.2020.02.018"},{"key":"e_1_3_4_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105621"},{"key":"e_1_3_4_20_2","doi-asserted-by":"crossref","unstructured":"Dharmender KumarP. Feature Extraction and Selection of Kidney Ultrasound Images using GLCM and PCA. ScienceDirect Procedia Computer Science. 2020; 167: 1722-1731.","DOI":"10.1016\/j.procs.2020.03.382"},{"key":"e_1_3_4_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107918"},{"key":"e_1_3_4_22_2","article-title":"Research on Medical Data Feature Extraction and Intelligent Recognition Technology based on Convolution Neural Network","volume":"7","author":"Liu W","year":"2019","unstructured":"LiuW QinC GaoK LiH QinZ CaoY SiW. Research on Medical Data Feature Extraction and Intelligent Recognition Technology based on Convolution Neural Network. Special Section on Data-Enabled Intelligence for Digital Health. 2019; 7.","journal-title":"Special Section on Data-Enabled Intelligence for Digital Health."},{"key":"e_1_3_4_23_2","first-page":"2109","article-title":"Deep Learning Quantification of Ovary and Follicles using 3D Transvaginal Ultrasound in Assisted Reproduction","author":"Mathur P","year":"2020","unstructured":"MathurP KakwaniK, DiplavKudavellyS. Deep Learning Quantification of Ovary and Follicles using 3D Transvaginal Ultrasound in Assisted Reproduction. IEEE Explore. 2020; 2109-2112.","journal-title":"IEEE Explore."},{"key":"e_1_3_4_24_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40738-019-0067-7"},{"key":"e_1_3_4_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2946092"},{"key":"e_1_3_4_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103545"},{"key":"e_1_3_4_27_2","first-page":"1","article-title":"An effective digit recognition model using enhanced convolutional neural network based on chaotic grey wolf optimization","author":"Preethi P","year":"2021","unstructured":"PreethiP AsokanR ThillaiarasuN SaravananT. An effective digit recognition model using enhanced convolutional neural network based on chaotic grey wolf optimization. Journal of Intelligent & Fuzzy Systems. (Preprint), 2021; 1-11.","journal-title":"Journal of Intelligent & Fuzzy Systems. (Preprint)"},{"key":"e_1_3_4_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-01939-7"},{"key":"e_1_3_4_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enggeo.2020.105972"},{"issue":"2","key":"e_1_3_4_30_2","article-title":"Deep learning frameworks for computer aided diagnosis based on medical images","volume":"82","author":"Zhang Q","year":"2021","unstructured":"ZhangQ. Deep learning frameworks for computer aided diagnosis based on medical images. Dissertation abstracts international. B, The Sciences and Engineering. 2021; 82(2).","journal-title":"Dissertation abstracts international. B, The Sciences and Engineering."},{"key":"e_1_3_4_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.04.011"}],"container-title":["Technology and Health Care"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/THC-230935","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/THC-230935","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/THC-230935","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:58:50Z","timestamp":1777615130000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/THC-230935"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":30,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["10.3233\/THC-230935"],"URL":"https:\/\/doi.org\/10.3233\/thc-230935","relation":{},"ISSN":["0928-7329","1878-7401"],"issn-type":[{"value":"0928-7329","type":"print"},{"value":"1878-7401","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9]]}}}