POLYCYSTIC OVARY SYNDROME DETECTION USING ULTRASOUND IMAGE BASED ON DANDELION SIBERIAN TIGER OPTIMISATION ENABLED ENSEMBLE CLASSIFIERS.
This study proposes an intelligent framework for Polycystic Ovary Syndrome (PCOS) detection using ultrasound images. An ensemble of deep learning models—ResNeSt, SA-Net, and DKN—optimized with Dandelion Siberian Tiger Optimization (DSTO) enhances diagnostic accuracy. Image preprocessing, DBSCAN-base...
| Publicado en: | Scientific Culture Vol. 12; no. 2, Part 1; pp. 3163 - 3171 |
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| Autores principales: | , |
| Formato: | Artículo |
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University of the Aegean
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=191995949&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 191995949 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 24080071 I6HU jtl: Scientific Culture issn: 24080071 maglogo: N pubinfo: dt: 2026 vid: 12 iid: 2, Part 1 pid: 47715 pub: University of the Aegean artinfo: ui: 191995949 10.5281/zenodo.122.126244 ppf: 3163 ppct: 8 formats: tig: atl: POLYCYSTIC OVARY SYNDROME DETECTION USING ULTRASOUND IMAGE BASED ON DANDELION SIBERIAN TIGER OPTIMISATION ENABLED ENSEMBLE CLASSIFIERS. aug: au: Deshmukh, Trupti Verma, Rakesh affil: Computer Science and Engineering Sanjeev Agrawal Global Educational University, Bhopal, India. su: Polyendocrine metabolic ovarian syndrome Ultrasonic imaging Computer-aided diagnosis Image segmentation Optimization algorithms Deep learning Ensemble learning sug: subj: Polyendocrine metabolic ovarian syndrome Ultrasonic imaging Computer-aided diagnosis Image segmentation Optimization algorithms Deep learning Ensemble learning keyword: Deep Learning Ensemble Models MATLAB Medical Image Classification Ultrasound Image Analysis ab: This study proposes an intelligent framework for Polycystic Ovary Syndrome (PCOS) detection using ultrasound images. An ensemble of deep learning models—ResNeSt, SA-Net, and DKN—optimized with Dandelion Siberian Tiger Optimization (DSTO) enhances diagnostic accuracy. Image preprocessing, DBSCAN-based segmentation, and feature extraction improve performance, achieving 96.32% accuracy and promising reliable, automated PCOS diagnosis in clinical applications. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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