肺癌和结肠癌的检测使用XRx-Net与组织病理学图像.
Zulaikha Beevi Sulaiman1, Sarojini Balakrishnan2, Rama Gaikwad3
1Department of AI&DS, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India.
Microscopy research and technique
|March 13, 2026
概括
一个新的Xception ResNeXT网络 (XRx-Net) 框架增强了肺癌和结肠癌 (LCC) 的检测,使用对组织病理图像的深度学习. 这种AI方法实现了高精度,提高了LCC的诊断效率.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 肺癌和结肠癌 (LCC) 诊断传统上依赖于组织病理学,这是一个复杂且耗时的显微镜检查.
- 遗传学,吸烟和饮酒等因素影响LCC的流行.
- 当前的诊断方法在效率和复杂性方面存在挑战.
研究的目的:
- 引入Xception ResNeXT网络 (XRx-Net) 框架,以有效检测肺癌和结肠癌 (LCC).
- 通过使用深度学习技术,提高LCC诊断的准确性和速度.
- 开发一个自动化系统来分类LCC亚型和良性组织.
主要方法:
- 用适应双边波器 (ABF) 预处理LCC的组织病理图像.
- 癌细胞使用hookNet进行细分,然后通过波形纹理分析和卷积神经网络 (CNN) 进行特征提取.
- 使用XRx-Net框架,Xception和ResNeXt模型的混合,用于LCC分类.
主要成果:
- 通过XRx-Net框架,成功识别了肺部良性组织,肺腺癌,肺状细胞癌,结肠良性组织和结肠腺癌.
- 取得了91.34%的测试准确度.
- 证明真正阳性率 (TPR) 为92.36%,真负率 (TNR) 为90.14%.
结论:
- XRx-Net框架为LCC检测提供了一个高度准确和高效的AI驱动的解决方案.
- 这种深度学习方法可以显著帮助病理学家诊断LCC亚型.
- 该研究强调了混合CNN架构在改善癌症诊断工作流程方面的潜力.
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