DKCNN:从胸部CT图像改进基于深核卷积神经网络的COVID-19识别
T Vaikunta Pai1, K Maithili2, Ravula Arun Kumar3
1Department of Information Science and Engineering, NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University), Bangalore, Karnataka, India.
Journal of X-ray science and technology
|May 31, 2024
概括
这项研究引入了一种高效的深卷积神经网络 (DeepCNN),用于从CT扫描中准确地分类COVID-19. 与现有方法相比,拟议的模型表现出优越的性能.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 准确和早期分类COVID-19对于有效的患者管理至关重要.
- 深层卷积神经网络 (DeepCNNs) 显示出医学图像分析的前景.
- 使用CT扫描进行COVID-19分类的现有方法需要提高效率和准确性.
研究的目的:
- 通过CT扫描图像提出一个高效的DeepCNN用于COVID-19分类.
- 为增强特征提取开发一种新的点向-时间-点向卷积单元.
- 使用Slap Swarm算法 (SSA) 来优化分类性能.
主要方法:
- 开发了一种新的点向-时间向-点向卷积单元,具有可变的基于内核的深度智能时间卷积.
- 预处理SARS-COV-2CT扫描数据集和CT扫描COVID预测数据集使用min-max规范化.
- 集成深度智能时间卷积,内核变异和步骤卷积,以改善分类和降低复杂性.
主要成果:
- 拟议的DeepCNN实现了对COVID-19疾病的有效分类.
- 实验分析表明,拟议的方法表现优于几种最先进的方法.
- 将深度时间卷曲和内核变异纳入,显著改善了分类.
结论:
- 开发的Pointwise-Temporal-pointwise卷积单元增强了COVID-19分类的深度CNN性能.
- 拟议的DeepCNN架构为疾病检测提供了一个高效和准确的解决方案.
- 剩余链接中的脚步卷曲有效地降低了计算复杂性.
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