通过使用深度学习技术的整微方程来检测癌症的综合框架
Tanneeru Gopisairam1, Srinivasarao Thota2, Thulasi Bikku3
1Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh, 522503, India.
Scientific reports
|February 18, 2026
概括
这项研究引入了一种新的深度学习框架,用于在乳房影像中检测癌症. 通过将2D图像转换为1D信号,它可以在识别癌症区域方面实现高精度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 从医学图像中准确诊断癌症至关重要,但具有挑战性.
- 深度学习为提高诊断准确性提供了有前途的计算工具.
研究的目的:
- 开发和评估一个新的框架,用于检测癌症在乳房影像.
- 用数学模型提高计算癌症诊断的解释性.
主要方法:
- 将二维乳房影像转换为一维信号以提取特征.
- 使用1D卷积神经网络进行图像分类.
- 结合整微分方程来建模瘤动态和强度变化.
主要成果:
- 在INbreast和MIAS数据集上的二进制分类中获得了96.4%的准确性.
- 与传统的深度学习基准表现相似或优越的表现.
- 在特征提取和计算效率方面的突出优势.
结论:
- 拟议的框架显示出在乳房影像中进行准确和可解释的癌症诊断的巨大潜力.
- 需要进一步的研究来解决诸如数据依赖性和信号转换期间信息丢失等局限性.
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