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先进的图像预处理和上下文感知空间分解,以增强乳腺癌细分的功能.

G Kalpana1, N Deepa1, D Dhinakaran2

  • 1Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India.

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|March 27, 2025
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概括

本研究介绍了先进的图像预处理技术 (AIPT) 和情境感知空间分解网络 (CASDN),以改善医学图像中的乳腺癌细分. 综合方法提高了诊断准确度和瘤边缘识别.

关键词:
AIPT (先进的图像预处理技术) 和CASDN (情境感知空间分解网络)增强 增强是一种增强.乳腺癌 乳腺癌 乳腺癌均等化方式 均等化方式多个规模的区域增强增强.规范化 规范化 规范化分段化 分段化 分段化 分段化

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 乳腺癌诊断依赖于医学成像,但噪音,低对比度和分辨率等挑战阻碍了恶性部位的准确细分.
  • 现有的细分方法与图像工件作斗争,影响诊断可靠性.

研究的目的:

  • 开发和评估一个综合解决方案,结合先进的图像预处理技术 (AIPT) 和情境感知空间分解网络 (CASDN),以改善乳腺癌细分.
  • 为了提高医疗图像的清晰度和减少扭曲,以便更好地识别瘤.

主要方法:

  • 采用了全面的AIPT管道,包括自适应值,层次对比度规范化,上下文特征增强,多尺度区域增强和动态直方体等级化.
  • 然后将预处理的图像输入CASDN进行细分.
  • 卷积神经网络被用于分类任务.

主要成果:

  • 提出的方法实现了0.89的子系数,0.85的IOU和5.2的豪斯多夫距离,证明了优越的瘤边缘细分.
  • 使用增强预处理管道的分类模型实现了85.3%的准确性和AUC-ROC为0.90.
  • 该系统在各种成像方式 (如乳房影像,超声波和MRI扫描) 中显示出强大的兼容性.

结论:

  • 整合AIPT和CASDN显著提高了乳腺癌细分精度和分类性能.
  • 先进的预处理管道有效地减轻了图像噪声和扭曲,从而导致更清晰的医学图像.
  • 开发的技术为跨多种成像模式的乳腺癌诊断提供了强大而有效的解决方案.