Abstract
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.
Abstract (translated)
多囊卵巢综合症（PCOS）是育龄妇女中常见的疾病，其特征包括激素失衡、月经不规律以及多个卵巢囊肿。长期的并发症如不孕症、代谢综合征和心血管风险凸显了早期检测的重要性。本文设计了一个基于迁移学习框架的方法，利用DenseNet201和ResNet50模型对卵巢超声图像进行分类。该模型在包含3856张囊性病变患者及非病变患者的在线数据集上进行了训练。每帧超声图被调整为224x224像素，并用精确的病理指标编码。为了提高泛化能力，采用了MixUp和CutMix增强策略，使得Densenet201模型达到峰值验证准确率99.80%，同时ResNet50模型在alpha值分别为0.25和0.4时取得了0.617的验证损失。 我们使用领先的可解释性人工智能（XAI）方法如SHAP、Grad-CAM以及LIME来评估该模型的可解释性，通过提供具体的视觉理由来说明模型的行为，从而增加了模型透明度。本研究提出了一种自动化的医学影像诊断系统，可以在临床实践中有效且自信地应用。 本文的研究为基于超声图像的PCOS早期检测提供了高效、准确的方法，并进一步探讨了如何提高深度学习在医疗领域应用中的可解释性与可靠性。
URL
https://arxiv.org/abs/2602.04944
