使用卷积神经网络集成的侵入性管道癌乳腺癌等级分类
Eelandula Kumaraswamy1, Sumit Kumar1,2, Manoj Sharma3
1School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara 144411, Punjab, India.
Diagnostics (Basel, Switzerland)
|June 10, 2023
概括
这项研究开发了一个使用卷积神经网络 (CNN) 的AI驱动系统,用于早期检测侵入性管道癌乳腺癌 (IDC-BC). 整体模型在分类IDC-BC等级方面实现了94%的准确性,改善了患者的治疗结果.
科学领域:
- 人工智能在医学中的应用
- 机器学习用于医学诊断
- 数字病理学数字病理学
背景情况:
- 侵袭性管道癌乳腺癌 (IDC-BC) 是最常见的癌症类型,其通常无症状的性质导致全球高死亡率.
- 支持人工智能的计算机辅助诊断 (CAD) 系统正在通过帮助病理学家早期发现疾病并提高诊断可靠性来彻底改变医学诊断.
- 准确的IDC-BC分级对于有效的患者治疗策略至关重要.
研究的目的:
- 探索预先训练的卷积神经网络 (CNN) 对侵入性管道癌乳腺癌 (IDC-BC) 等级分类的有效性.
- 评估单个CNN模型 (EfficientNetV2L,ResNet152V2,DenseNet201) 及其组合在IDC-BC分级中的性能.
- 评估数据增强和不同数据集大小对模型性能的影响.
主要方法:
- 使用预先训练的CNN模型:EfficientNetV2L,ResNet152V2和DenseNet201,无论是单独的还是作为一个合奏.
- 采用数据增强技术来解决DataBiox数据集中的数据稀缺和不平衡问题.
- 在不同大小的平衡数据集 (1200,1400,1600张图像) 上评估模型性能,并分析训练时代的影响.
主要成果:
- 拟议的组合模型在DataBiox数据集上的IDC-BC等级上实现了94%的分类准确性.
- 整体模型显示了ROC曲线下的面积 (AUC) 有意义的值:96%的等级1,94%的等级2和96%的等级3.
- 整体方法的性能优于IDC-BC等级分类的现有最先进方法.
结论:
- 预先训练有素的CNN,特别是在合奏配置中,显示出对准确的IDC-BC等级分类有很大的潜力.
- 数据增强有效地减轻了数据稀缺性和不平衡,从而提高了模型的稳定性.
- 开发的人工智能系统为病理学家提供了一个有前途的工具,有可能导致更早的诊断和更好的患者治疗结果 侵入性管道癌 乳腺癌.
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