对于学习胸部X射线和CT图像中的不平衡数据的动态学习.
Saeed Iqbal1,2, Adnan N Qureshi2, Jianqiang Li1,3
1Faculty of Information Technology, Beijing University of Technology, Beijing, 100124,China.
Heliyon
|June 14, 2023
概括
这项研究引入了一种新的类平衡技术 (3-Phase动态学习) 和一个并行的CNN模型 (混合特征融合) 来从医疗图像中检测肺部疾病. 该方法有效地解决了数据不平衡,实现了高精度,以改善诊断支持.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 深度学习模型需要大量的注释数据集,这些数据集对于病毒流行病等新型研究领域来说通常是有限和不平衡的.
- 从胸部X射线和CT扫描中检测肺部疾病是具有挑战性的,因为数据稀缺和阶级不平衡,特别是对于罕见或严重的病例.
研究的目的:
- 开发一个强大的深度学习框架,从医学图像中准确检测肺部疾病,特别是解决不平衡数据集的挑战.
- 加强在有限的医学图像数据集中识别少数群体 (例如,新型疾病的重大病例).
主要方法:
- 一种新的类平衡算法,三相动态学习 (3PDL),被开发来处理不平衡的数据集.
- 一个并行的卷积神经网络 (CNN) 模型,混合特征融合 (HFF),被用于多模式图像分析 (X射线和CT).
- 支持矢量机 (SVM) 用于集群过程中的图像分类,概率模型代表了数据特征.
主要成果:
- 拟议的3PDL和HFF模型实现了高F1得分96.83%和96.87%的精度.
- 该系统在识别肺部疾病迹象方面表现出卓越的准确性和概括能力.
- 基于不平衡的样本分析仪有效地识别了少数群体类别,提高了分类性能.
结论:
- 开发的技术成功地解决了用于肺部疾病检测的医学图像分析中的阶级不平衡问题.
- 3PDL和HFF模型的高性能表明它们作为医生和病理学家的支持工具的实用性.
- 这种方法为改善在医疗数据有限和不平衡的情况下的诊断准确性提供了有希望的解决方案.
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