Polycystic Ovary Syndrome (PCOS) is the most familiar endocrine illness in women of reproductive age. Many Bangladeshi women suffer from PCOS disease in their older age. The aim of our research is to identify effective vision-based medical image analysis techniques and evaluate hybrid models for the accurate detection of PCOS. We introduced two novel hybrid models combining convolutional and transformer-based approaches. The training and testing data were organized into two categories: "infected" (PCOS-positive) and "noninfected" (healthy ovaries). In the initial stage, our first hybrid model, 'DenConST' (integrating DenseNet121, Swin Transformer, and ConvNeXt), achieved 85.69% accuracy. The final optimized model, 'DenConREST' (incorporating Swin Transformer, ConvNeXt, DenseNet121, ResNet18, and EfficientNetV2), demonstrated superior performance with 98.23% accuracy. Among all evaluated models, DenConREST showed the best performance. This research highlights an efficient solution for PCOS detection from ultrasound images, significantly improving diagnostic accuracy while reducing detection errors.

翻译：多囊卵巢综合征（PCOS）是育龄期女性最常见的内分泌疾病。许多孟加拉国女性在年龄增长后罹患此病。本研究旨在识别有效的基于视觉的医学影像分析技术，并评估用于精准检测PCOS的混合模型。我们提出了两种结合卷积与Transformer架构的新型混合模型。训练与测试数据被划分为“感染”（PCOS阳性）和“未感染”（健康卵巢）两类。在初始阶段，首个混合模型'DenConST'（整合DenseNet121、Swin Transformer与ConvNeXt）取得了85.69%的准确率。最终优化模型'DenConREST'（融合Swin Transformer、ConvNeXt、DenseNet121、ResNet18及EfficientNetV2）展现出更优异的性能，准确率达98.23%。在所有评估模型中，DenConREST表现最佳。本研究为基于超声图像的PCOS检测提供了一种高效解决方案，在显著提升诊断准确率的同时降低了检测误差。