利用转移学习和注意力机制为计算机断层扫描肺癌分类模型
Kian A Huang1, Vishnu Venkitasubramony1, Neelesh S Prakash1
1Radiology, University of South Florida Morsani College of Medicine, Tampa, USA.
Cureus
|August 1, 2025
概括
使用ResNet50V2和SE块的深度学习模型从CT扫描中准确分类肺癌亚型,有助于早期检测和诊断. 这种人工智能工具有望提高放射科医生的效率,特别是在专家稀缺的地方.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 肺癌是全球癌症死亡的主要原因之一.
- 晚期诊断显著影响患者的生存率.
- 人工智能,特别是深度学习,有可能提高放射学诊断的准确性和效率.
研究的目的:
- 开发和评估一种深度学习模型,用于自动化CT图像的肺癌亚型分类.
- 该模型将剩余网络50版本2 (ResNet50V2) 与挤压和激发 (SE) 块集成在一起.
- 目标是提高放射学诊断能力.
主要方法:
- 使用了1000张肺部CT图像的数据集,分为腺癌,大细胞癌,状细胞癌和正常组织.
- 一个微调的ResNet50V2架构与SE块被用于特征重新校准.
- 该模型使用标准机器学习指标进行训练和评估,包括准确性,AUC,精度,回忆和F1分数.
主要成果:
- 该模型实现了90.16%的测试精度和0.9815.15的整体AUC.
- 观察到高类明智的AUC,值在0.9523到0.9977.7之间.
- 所有肺癌亚型和正常组织的高精度,回忆和F1分数都表明了强大的表现.
结论:
- 具有SE块的ResNet50V2模型在CT图像的肺癌亚型分类方面表现出高的性能.
- 这种人工智能方法显示了协助放射科医生的潜力,特别是在资源有限的环境中.
- 未来的研究应该包括外部验证和探索视觉转换器等先进架构.
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