Advanced holographic convolutional dense networks and Tangent runner optimization for enhanced polycystic ovarian disease classification

Advanced holographic convolutional dense networks and Tangent runner optimization for enhanced polycystic ovarian disease classification

Abstract Polycystic Ovarian Disease (PCOD) is among the most prevalent endocrine disorders complicating the health of innumerable women worldwide due to lack of diagnosis and appropriate management. The diagnosis of PCOD, along with proper classification with the help of ultrasound imaging, would be...

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Main Authors: Prathibanandhi Jeyashanker, Annie Grace Vimala Georgewilliam Sundaram, Padmakala Sadagopan, Abid Yahya, Ravi Samikannu, Irfan Anjum Badruddin, Sarfaraz Kamangar, Mohamed Ghouse Shukur
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Polycystic ovarian disease
Ultrasound images
Medical imaging
Disease diagnosis
Deep learning
Optimization
Online Access:https://doi.org/10.1038/s41598-025-98873-5
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https://doi.org/10.1038/s41598-025-98873-5

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