优化的CNN框架与VGG19,EfficientNet和贝叶斯优化用于早期结肠癌检测
Tawfikur Rahman1, Nibedita Deb2, Samia Larguech3
1Department of Electrical and Electronic Engineering, Faculty of Engineering, International University of Business Agriculture and Technology, Uttara, Dhaka, 1230, Bangladesh.
Scientific reports
|January 6, 2026
概括
这项研究提出了一个深度学习框架,用于在组织病理学图像中自动检测结肠癌. 这种先进的模型实现了高精度,为早期癌症诊断提供了一个有前途的工具,并帮助病理学家.
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:
- 结肠癌仍然是全球癌症死亡的主要原因.
- 早期和准确的检测方法对于改善患者的治疗结果至关重要.
- 组织病理学图像分析对于癌症诊断至关重要.
研究的目的:
- 开发一个先进的深度学习框架,用于自动化结肠癌识别.
- 为了提高分类准确度,并最大限度地减少过度匹配在组织病理图像分析.
- 为病理学家创建一个强大的计算机辅助诊断 (CAD) 工具.
主要方法:
- 卷积神经网络 (CNN) 与贝叶斯优化用于超参数调整的集成.
- 在Kaggle和KCDP的合并数据集上进行培训和测试,包括九种组织类型.
- 实施数据增强和污点规范化技术,以提高概括性.
主要成果:
- 优化的CNN实现了96.84%的准确性,97.02%的精度,96.50%的回忆和96.71%的F1分数.
- 曲线下的面积 (AUC) 值为0.97,表明具有较高的区分能力.
- 拟议的方法在稳定性和通用性方面超过了基线CNN和ResNet架构.
结论:
- 深度学习框架显示出作为用于结肠癌诊断的CAD工具的显著前景.
- 该模型的有效性可能通过转移学习扩展到其他癌症类型.
- 在临床部署之前,对多机构队列的外部验证是必要的.
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