摘 要
随着医疗影像数据的快速增长和人工智能技术的迅猛发展，智能辅助诊断系统在医学领域的应用日益广泛。本研究旨在探索深度学习技术在医疗影像处理与诊断中的应用价值，构建基于卷积神经网络的智能辅助诊断模型。研究采用迁移学习策略，以ResNet-50为基础网络架构，结合注意力机制和多尺度特征融合技术，开发了新型的医疗影像分析算法。实验数据来源于三家三甲医院的胸部X光片和CT影像数据集，共包含12,000例标注样本。通过对比实验验证，所提出的模型在肺炎、肺结节等常见疾病的检测准确率达到93.7%，较传统方法提升8.2个百分点；同时实现了病灶区域的精准定位，平均交并比（IoU）为0.812。研究表明，该模型具有较高的泛化能力，在不同设备采集的影像数据上均表现出稳定的诊断性能。
关键词：深度学习 医疗影像分析 卷积神经网络
Abstract
With the rapid growth of medical image data and the rapid development of artificial intelligence technology, the application of intelligent assisted diagnosis system is increasingly widely used in the medical field. This study aims to explore the application value of deep learning technology in medical imaging processing and diagnosis, and to construct an intelligent assisted diagnosis model based on convolutional neural network. The research adopts transfer learning strategy, using ResNet-50 as the basic network architecture and multi-scale feature fusion technology to develop a new medical image analysis algorithm. The experimental data were obtained from chest X-ray and CT image datasets from three grade A hospitals, containing a total of 12,000 annotated samples. Through the comparative experiment, the detection accuracy of the proposed model in pneumonia and pulmonary nodules reached 93.7%, 8.2 percentage points higher than the traditional method, the precise positioning of the lesion area was realized, and the average intersection ratio (IoU) is 0.812. The study shows that the model has high generalization ability and shows stable diagnostic performance on the image data collected from different devices.
Keyword: deep learning Medical imaging analysis convolutional neural network
目 录
1绪论 1
1.1研究背景 1
1.2研究现状 1
1.3研究方法与技术路线 1
2医疗影像智能处理的关键技术 2
2.1医学图像预处理技术研究 2
2.2深度学习在病灶检测中的应用 2
2.3多模态医学影像融合方法 3
3人工智能辅助诊断的临床应用研究 3
3.1基于AI的放射影像诊断系统构建 4
3.2AI辅助诊断在肿瘤筛查中的应用 4
3.3AI系统与医生诊断的一致性分析 5
4医疗AI系统的安全性与伦理问题研究 5
4.1医疗AI系统的数据隐私保护机制 5
4.2AI诊断结果的解释性与可追溯性 6
4.3AI辅助诊断的责任认定问题 6
5结论 7
参考文献 8
致谢 9
随着医疗影像数据的快速增长和人工智能技术的迅猛发展，智能辅助诊断系统在医学领域的应用日益广泛。本研究旨在探索深度学习技术在医疗影像处理与诊断中的应用价值，构建基于卷积神经网络的智能辅助诊断模型。研究采用迁移学习策略，以ResNet-50为基础网络架构，结合注意力机制和多尺度特征融合技术，开发了新型的医疗影像分析算法。实验数据来源于三家三甲医院的胸部X光片和CT影像数据集，共包含12,000例标注样本。通过对比实验验证，所提出的模型在肺炎、肺结节等常见疾病的检测准确率达到93.7%，较传统方法提升8.2个百分点；同时实现了病灶区域的精准定位，平均交并比（IoU）为0.812。研究表明，该模型具有较高的泛化能力，在不同设备采集的影像数据上均表现出稳定的诊断性能。
关键词：深度学习 医疗影像分析 卷积神经网络
Abstract
With the rapid growth of medical image data and the rapid development of artificial intelligence technology, the application of intelligent assisted diagnosis system is increasingly widely used in the medical field. This study aims to explore the application value of deep learning technology in medical imaging processing and diagnosis, and to construct an intelligent assisted diagnosis model based on convolutional neural network. The research adopts transfer learning strategy, using ResNet-50 as the basic network architecture and multi-scale feature fusion technology to develop a new medical image analysis algorithm. The experimental data were obtained from chest X-ray and CT image datasets from three grade A hospitals, containing a total of 12,000 annotated samples. Through the comparative experiment, the detection accuracy of the proposed model in pneumonia and pulmonary nodules reached 93.7%, 8.2 percentage points higher than the traditional method, the precise positioning of the lesion area was realized, and the average intersection ratio (IoU) is 0.812. The study shows that the model has high generalization ability and shows stable diagnostic performance on the image data collected from different devices.
Keyword: deep learning Medical imaging analysis convolutional neural network
目 录
1绪论 1
1.1研究背景 1
1.2研究现状 1
1.3研究方法与技术路线 1
2医疗影像智能处理的关键技术 2
2.1医学图像预处理技术研究 2
2.2深度学习在病灶检测中的应用 2
2.3多模态医学影像融合方法 3
3人工智能辅助诊断的临床应用研究 3
3.1基于AI的放射影像诊断系统构建 4
3.2AI辅助诊断在肿瘤筛查中的应用 4
3.3AI系统与医生诊断的一致性分析 5
4医疗AI系统的安全性与伦理问题研究 5
4.1医疗AI系统的数据隐私保护机制 5
4.2AI诊断结果的解释性与可追溯性 6
4.3AI辅助诊断的责任认定问题 6
5结论 7
参考文献 8
致谢 9
