用深度学习自动诊断多囊卵巢综合征，准确率达99.8%。
Smart Diagnosis and Early Intervention in PCOS: A Deep Learning Approach to Women's Reproductive Health
- 基于DenseNet201和ResNet50的迁移学习框架，处理超声图像分类。
- 在3856张图像上达到99.80%验证准确率，损失为0.617。
- 结合SHAP、Grad-CAM等可解释AI技术，提升临床可信度。
多囊卵巢综合征（PCOS）是育龄女性中常见的内分泌紊乱疾病，表现为激素失衡、月经不规律及多个卵巢囊肿。不孕、代谢综合征和心血管风险是其长期并发症，因此早期检测至关重要。本文设计了一种基于迁移学习的深度学习框架，采用DenseNet201和ResNet50对卵巢超声图像进行分类。模型在包含3856张囊肿感染与非感染患者超声图像的在线数据集上训练，每帧图像被缩放至224×224像素，并标注精确病理指标。使用MixUp和CutMix增强策略提升泛化能力，DenseNet201达到99.80%的峰值验证准确率，验证损失为0.617，对应alpha值分别为0.25和0.4。通过SHAP、Grad-CAM和LIME等可解释AI方法评估模型可解释性，明确呈现模型决策的视觉依据，增强透明性。该研究提出一个可在临床实践中有效且可信使用的自动化医学影像诊断系统。
原文摘要 · Abstract (English)
Polycystic Ovary Syndrome (PCOS) is a widespread disorder in women of reproductive age, characterized by a hormonal imbalance, irregular periods, and multiple ovarian cysts. Infertility, metabolic syndrome, and cardiovascular risks are long-term complications that make early detection essential. In this paper, we design a powerful framework based on transfer learning utilizing DenseNet201 and ResNet50 for classifying ovarian ultrasound images. The model was trained on an online dataset containing 3856 ultrasound images of cyst-infected and non-infected patients. Each ultrasound frame was resized to 224x224 pixels and encoded with precise pathological indicators. The MixUp and CutMix augmentation strategies were used to improve generalization, yielding a peak validation accuracy of 99.80% by Densenet201 and a validation loss of 0.617 with alpha values of 0.25 and 0.4, respectively. We evaluated the model's interpretability using leading Explainable AI (XAI) approaches such as SHAP, Grad-CAM, and LIME, reasoning with and presenting explicit visual reasons for the model's behaviors, therefore increasing the model's transparency. This study proposes an automated system for medical picture diagnosis that may be used effectively and confidently in clinical practice.
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