一个轻量级的CNN用于结肠癌组织分类和可视化.
Jie Li1, Weiwei Goh1, Noor Zaman Jhanjhi1
1Digital Health and Medical Advancement Impact Lab, School of Computer Science, Taylor's University, Subang Jaya, Malaysia.
Frontiers in oncology
|November 3, 2025
概括
一个新的轻量级卷积神经网络 (CNN) 模型准确地分类结肠癌 (CC) 组织病理图像. 这种人工智能工具提高了病理学诊断的准确性和效率,有助于未来的癌症检测.
科学领域:
- 医疗成像医学成像
- 计算病理学计算病理学
- 在瘤学中使用人工智能
背景情况:
- 结肠癌 (CC) 诊断在很大程度上依赖于组织病理学图像分析.
- 计算医学解决方案对于现代放射学和病理学来说越来越重要.
- 准确高效的图像分类对于及时检测结肠癌至关重要.
研究的目的:
- 介绍一种新的,轻量级的卷积神经网络 (CNN) 模型用于结肠癌组织病理学图像分类.
- 制定有效的数据清理策略,以提高图像数据质量.
- 为结肠癌诊断提供一种资源高效的模型.
主要方法:
- 开发一个非预训练,轻量级的CNN架构.
- 实施基于参数高斯分布的数据清理方法以删除异常值.
- 使用NCT-CRC-HE-100K和CRC-VAL-HE-7K数据集进行培训和验证.
主要成果:
- 该模型实现了0.990 ± 0.003.3的高测试精度.
- 在各种组织类别中表现出卓越的精度,回忆,特异性和F1分数.
- 这款轻量级模型包含4,414,217个参数,大小为16.9 MB.
结论:
- 开发的CNN模型为结肠癌组织病理学图像分类提供了强大的轻量级解决方案.
- 数据清理策略有效地提高了分类性能.
- 这项研究为通过人工智能和图像可视化推进结肠癌诊断提供了基础.
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