医疗应用驱动的基于内容的医疗图像检索系统,用于对X射线图像进行增强分析
1Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, 611002, India. saranyaedumban@gmail.com.
Scientific reports
|August 8, 2025
概括
这项研究引入了深度学习 (DL) 技术,用于准确的医学图像检索,克服了数据集可用性的局限性. 这种先进的模型在复杂的医学成像数据集中增强了基于内容的图像检索 (CBIR).
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 基于内容的图像检索 (CBIR) 通常需要大型,平衡的数据集,这些数据集往往无法在医学领域获得.
- 现有的CBIR方法难以应对医疗图像数据的复杂性和可变性.
- 准确检索医疗图像对于诊断和治疗计划至关重要.
研究的目的:
- 提出一种先进的深度学习 (DL) 技术,以提高复杂的医疗数据集中图像检索的准确性.
- 开发一个强大的模型,能够克服传统CBIR在专业医疗领域的局限性.
- 通过增强的检索能力,提高医学图像分析的效率和可靠性.
主要方法:
- 开发了一个五个阶段的DL模型:预处理,图像分解,特征提取,维度缩小和归类与检索.
- 混合波形哈达马德转换 (HWHT) 用于捕获低频和高频细节.
- 灰色水平共发生矩阵 (GLCM) 用于特征提取,基于正弦混沌的人工子优化 (SCARO) 用于维度降低,和Bhattacharya 环境性能意识的基于全球关注的变压器 (BCGAT) 用Bhattacharyya 系数进行分类和相似性匹配.
主要成果:
- 该模型在COVID-19胸部X射线数据集上实现了高性能,准确率为99.5%,精度为97.1%,回忆和F1-Score.
- 在胸部X射线图像 (肺炎) 数据集上,该模型获得了98.60%的准确性,98.49%的精度,97.40%的回忆率和98.50%的F1-Score.
- 美国国家卫生研究院的胸部X射线数据集的准确率为99.67%.
结论:
- 拟议的DL技术显著提高了复杂的医疗数据集中的基于内容的图像检索的准确性.
- 该模型在各种医学成像数据集中表现出卓越的性能,包括COVID-19和肺炎胸部X射线.
- 这种方法为改善医疗图像分析和检索系统提供了一个有希望的解决方案,在传统方法不足的情况下.
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