剩余-SwishNet:一种基于深度学习的方法,用于可靠的肺癌分类
Marriam Nawaz1, Ali Javed1, Abdul Khader Jilani Saudagar2
1Department of Software Engineering, University of Engineering and Technology-Taxila, Taxila, Pakistan.
Frontiers in oncology
|February 11, 2026
概括
这项研究介绍了Residual-SwishNet,这是一种用于肺癌分类的新型深度学习模型. 该模型实现了高精度,为早期肺癌诊断提供了强大的工具,并改善了患者的治疗结果.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 肺癌是全球癌症死亡的主要原因,需要改进的诊断工具.
- 当前的计算机辅助检测系统在特征提取,准确性和通用性方面面临着挑战.
研究的目的:
- 为准确可靠的肺癌分类开发一个深度学习模型.
- 为了提高功能提取和分类性能,用于肺癌检测.
主要方法:
- 开发了一个修改后的ResNet50架构,Residual-SwishNet.
- ReLU激活功能被Swish取代,并添加了三个密集的层,以丰富功能表示.
- 使用交叉损的软max输出来解决类不平衡.
主要成果:
- 在Luna16上,Residual-SwishNet实现了高分类准确率99.60%,在IQ-OTH/NCCD上达到99.11%.
- 与现有的最先进的技术相比,该模型表现出卓越的性能.
结论:
- 拟议的Residual-SwishNet模型显示出作为肺癌诊断的强大工具的巨大潜力.
- 这种深度学习方法为早期肺癌检测提供了更高的准确性和可靠性.
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