胸部X射线图像中肺炎的二进制分类使用修改后的对比度有限的自适应性直方图等级算法.
Abror Shavkatovich Buriboev1, Akmal Abduvaitov2, Heung Seok Jeon3
1Department of AI-Software, Gachon University, Seongnam-si 13120, Republic of Korea.
这项研究引入了自适应对比增强模型和卷积神经网络 (CNN),用于在胸部X射线中准确检测肺炎. 综合方法实现了98.7%的准确性,改善了诊断支持.
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:
- 肺炎是一个重要的全球健康问题,需要先进的诊断方法.
- 胸部X射线的自动分析可以帮助及时检测肺炎.
- 现有的方法可能缺乏可靠的自动诊断所需的精度.
研究的目的:
- 利用胸部X射线图像开发和评估一种用于肺炎二元分类的新方法.
- 通过自适应对比增强模型,提高图像质量,以改善肺炎检测.
- 评估与增强模型集成的卷积神经网络 (CNN) 的性能.
主要方法:
- 实现了自适应对比增强模型,其中包括自适应的尺寸,差异引导的剪切和权重的再分配.
- 将增强模型应用于胸部X射线图像 (肺炎) 数据集 (5856张图像).
- 关于肺炎分类增强图像的卷积神经网络 (CNN) 的培训和评估.
主要成果:
- 拟议的模型实现了高分类性能:98.7%的准确性,99.3%的精度,98.6%的回忆率和97.9%的F1分数.
- 与基线方法相比,自适应增强显著改善了CNN的性能.
- 五次交叉验证证实了模型的稳定性,特征可视化表明了临床相关性.
结论:
- 集成的自适应对比增强和CNN方法提供了一种可靠的方法,用于从胸部X射线进行自动肺炎分类.
- 这种技术显示出在医学成像中增强诊断支持系统的潜力.
- 未来的工作重点是验证模型在更大,更多样化的数据集上的可通用性.
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