优化CNN模型提升胸部疾病分类准确率
Optimizing CNN Architectures for Advanced Thoracic Disease Classification
- 对比多种CNN架构，结合主成分分析压缩图像
- 提出加权损失函数，有效缓解数据不平衡问题
- 适合医疗影像分析与深度学习初学者参考
机器学习，特别是卷积神经网络（CNN），在医学图像分析中展现出巨大潜力，尤其在利用胸片进行胸部疾病检测方面。本研究评估了多种CNN架构，包括二分类、多标签分类及ResNet50模型，以应对数据集不平衡、图像质量差异和隐含偏差等挑战。我们引入先进的预处理技术，如使用主成分分析（PCA）进行图像压缩，并提出一种新型类别加权损失函数，以缓解不平衡问题。结果表明，尽管CNN在医学影像中具有潜力，但需解决数据不平衡和成像方法差异等问题，才能实现最优性能。
原文摘要 · Abstract (English)
Machine learning, particularly convolutional neural networks (CNNs), has shown promise in medical image analysis, especially for thoracic disease detection using chest X-ray images. In this study, we evaluate various CNN architectures, including binary classification, multi-label classification, and ResNet50 models, to address challenges like dataset imbalance, variations in image quality, and hidden biases. We introduce advanced preprocessing techniques such as principal component analysis (PCA) for image compression and propose a novel class-weighted loss function to mitigate imbalance issues. Our results highlight the potential of CNNs in medical imaging but emphasize that issues like unbalanced datasets and variations in image acquisition methods must be addressed for optimal model performance.
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