用混合模型提升超声图诊断多囊卵巢综合征的准确率
Vision Models for Medical Imaging: A Hybrid Approach for PCOS Detection from Ultrasound Scans
- 融合卷积与视觉变压器的混合架构
- 最优模型达98.23%准确率，显著优于其他方法
- 适合医疗影像分析与辅助诊断研究者参考
多囊卵巢综合征（PCOS）是育龄女性最常见的内分泌疾病，许多孟加拉女性在年长后受到影响。本研究旨在识别有效的基于视觉的医学图像分析技术，并评估混合模型在PCOS精准检测中的表现。我们提出两种新型混合模型，结合卷积网络与视觉变压器。训练与测试数据分为两类：感染（PCOS阳性）与非感染（健康卵巢）。初期模型DenConST（集成DenseNet121、Swin Transformer与ConvNeXt）达到85.69%准确率；最终优化模型DenConREST（融合Swin Transformer、ConvNeXt、DenseNet121、ResNet18与EfficientNetV2）表现更优，准确率达98.23%。在所有评估模型中，DenConREST性能最佳。本研究为超声图像中的PCOS检测提供了高效解决方案，显著提升诊断准确性并降低误诊率。
原文摘要 · Abstract (English)
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.
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