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对口腔状细胞癌的深度视觉检测系统
Kainat Akram1, Muhammad Aslam1, Talha Waheed1
1Department of Computer Science, University of Engineering and Technology, Lahore, 54000, Pakistan.
Scientific reports
|January 19, 2026
概括
这项研究介绍了一种使用EfficientNetB3的深度视觉检测系统 (DVDS),用于自动检测口腔状细胞癌 (OSCC) 从基因病理图像. 该系统实现了高精度,证明了更快,更一致的OSCC诊断的潜力,以改善患者的治疗结果.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算病理学计算病理学
背景情况:
- 口腔状细胞癌 (OSCC) 是一个重大的健康挑战,需要准确和及时的诊断.
- 传统的组织病理学方法用于OSCC诊断是主观的和耗时的.
- 人工智能 (AI) 的进步为对病原体图像的客观和高效分析提供了潜力.
研究的目的:
- 开发和评估用于口腔状细胞癌 (OSCC) 检测的自动化深度视觉检测系统 (DVDS).
- 为了比较不同深度学习模型 (EfficientNetB3,DenseNet121,ResNet50) 的性能,用于OSCC分类.
- 评估系统的可靠性和稳定性在诊断OSCC从他的病理图像.
主要方法:
- 我们使用了三个卷积神经网络 (CNN) 模型:EfficientNetB3,DenseNet121和ResNet50.
- 在两个公共数据集上训练和评估模型:Kaggle口腔癌检测和NDB-UFES.
- 采用数据增强,图像预处理和训练策略,如早期停止和减少LROnPlateau.
主要成果:
- EfficientNetB3表现出卓越的性能,在二进制分类中达到97.05%的准确性,在多类分类中达到97.16%的准确性.
- 该系统在两个数据集中表现出高精度,回忆,F1得分和特异性.
- 与EfficientNetB3.3.相比,DenseNet121和ResNet50的准确性显著降低,而EfficientNetB3.3的准确性则明显降低.
结论:
- 由EfficientNetB3驱动的深度视觉检测系统 (DVDS) 在OSCC诊断中显示出高可靠性.
- 由人工智能驱动的方法可以显著简化诊断工作流程,并帮助病理学家.
- 这项技术具有很强的临床部署潜力,以支持早期干预和增强患者护理.
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