从基于Xception的转移学习和后勤回归的胸部X射线上检测肺炎
Muhammad Mujahid1, Furqan Rustam2, Prasun Chakrabarti3
1Artificial Intelligence & Data Analytics Lab, CCIS, Prince Sultan University, Riyadh 11586, Saudi Arabia.
概括
这项研究利用机器学习和数据增强,通过胸部X射线增强了肺炎检测. 使用Xception和VGG-16的转移学习实现了99.23%的准确性,改进了传统方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 在全球范围内,肺炎是导致死亡的首要原因,特别是在儿童和老年人中.
- 胸部X射线是主要的诊断工具,但放射科医生的解释是耗时的,容易出现错误,特别是与其他疾病 (如COVID-19) 相重叠的症状.
- 目前用于从X射线检测肺炎的机器学习模型与数据不平衡和泛化作斗争,限制了它们的准确性和稳定性.
研究的目的:
- 提高机器学习模型的准确性和稳定性,用于从胸部X射线检测肺炎.
- 通过数据增强技术解决数据不平衡问题.
- 为了评估转移学习对自动特征提取的有效性,与传统手工制作的特征相比.
主要方法:
- 使用预训练模型 (Xception和VGG-16) 的转移学习,自动从胸部X射线中提取特征.
- 采用数据增强来缓解数据不平衡问题.
- 训练并比较各种分类器,包括支持向量机,物流回归,K-最近邻居,随机梯度下降,额外树分类器和梯度提升机.
主要成果:
- 与手工制作的肺炎检测功能相比,基于转移学习的功能表现出更高的性能.
- 通过提议的转移学习方法,使用胸部X射线检测肺炎的高精度达到了99.23%.
- 该研究成功地解决了数据不平衡问题,从而使模型性能更加稳健.
结论:
- 转移学习为医学图像分析中的自动特征提取提供了一个强大的方法,显著提高了肺炎检测准确度.
- 数据增强对于提高处理医学诊断中不平衡数据集的机器学习模型的性能至关重要.
- 开发的方法提供了一个高度准确和高效的自动化解决方案,用于通过胸部X射线检测肺炎,这可能有助于临床决策.
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