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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...

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Publicado en:Scientific Culture Vol. 12; no. 2, Part 1; pp. 3163 - 3171
Autores principales: Deshmukh, Trupti, Verma, Rakesh
Formato: Artículo
Publicado: University of the Aegean 2026
Materias:
Polyendocrine metabolic ovarian syndrome
Ultrasonic imaging
Computer-aided diagnosis
Image segmentation
Optimization algorithms
Deep learning
Ensemble learning
Acceso en línea:Ver este registro en EBSCOhost
Sumario: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.