一种机器学习方法用于胸部X射线,用于儿童肺炎检测
Natali Barakat1, Mahmoud Awad2, Bassam A Abu-Nabah3
1Engineering Systems Management Department, American University of Sharjah, College of Engineering, Sharjah, United Arab Emirates.
Digital health
|June 14, 2023
概括
机器学习 (ML) 通过胸部X射线自动检测儿科肺炎,达到97.58%的准确性. 这种方法比深度学习更快,更易于解释,有助于早期诊断和降低儿童死亡率.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 儿童肺炎的诊断 儿童肺炎的诊断
背景情况:
- 肺炎是5岁以下儿童的主要感染性死亡原因之一.
- 早期发现儿科肺炎对于降低死亡率至关重要.
- 对于儿科肺炎的胸部X射线解释,观察者之间的协议很差,需要自动检测.
研究的目的:
- 为了自动化儿童肺炎的早期检测.
- 利用机器学习 (ML),因为它比深度学习 (DL) 具有较低的计算需求和更高的解释性.
主要方法:
- 使用数据增强来平衡数据集类.
- 优化了特征提取方案.
- 评估了多种ML模型,并与转移学习 (TL) 基准进行了比较.
主要成果:
- 方位支向量机 (SVM) 模型实现了97.58%的准确性,超过了现有的ML文献.
- ML模型显示分类时间比TL基准要快得多.
结论:
- 建议的ML方法对于可靠的儿科肺炎检测非常有效.
- 这种方法为自动肺炎诊断提供了一个计算效率高且可解释的替代方案.
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