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| Risk Prediction of Polycystic Ovary Syndrome Using Stamen-Pistil Convolutional Network |
| LUO Lie1, WANG Tao1, CHEN Bingjing1, WU Xiaoyuan2, LIN Zhongyan1 |
1. International Digital Economy College, Minjiang University,Fuzhou 350108;
2. College of Economics and Management, Minjiang University,Fuzhou 350108 |
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Abstract In polycystic ovary syndrome(PCOS) clinical data analysis, many models often suffer from underfitting, while convolutional neural networks are limited by their receptive fields. To address these issues, a stamen-pistil convolutional network(SPCNet) is proposed. The design of SPCNet is inspired by the collaborative structure of stamens and pistils in flowers. The pistil convolution passes vertically through the main information stream to extract global features, while the stamen convolution performs supplementary sampling from the horizontal neighborhood. The structure of floral reproductive organs is adopted in the design of convolutional operators to improve feature extraction. By expanding the sampling space in both longitudinal and transverse directions, SPCNet effectively compensates for the limitations of existing PCOS processing approaches. Experiments on two public PCOS datasets show that SPCNet improves computational efficiency and achieves higher prediction accuracy with lightweight structure, thereby better meeting the needs for instant analysis of complex PCOS clinical data.
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Received: 07 July 2025
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Corresponding Authors:
LIN Zhongyan, Ph.D., professor. Her research interests include digital health and management innovation.
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About author:: LUO Lie, Ph.D. candidate. His research interests include machine learning, deep learning, and generative artificial intelligence.
WANG Tao, Ph.D., associate professor. His research interests include computer vision, machine learning, scene understanding, object detection, and semantic segmentation.
CHEN Bingjing, Master. Her research interests include visual images and generation.
WU Xiaoyuan, Ph.D., associate profe-ssor. Her research interests include innovation management, digital health management, and transformation of digital technology applications. |
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2025, Vol. 38 
