一个灵活的多道深度网络,利用纹理和空间特征来诊断肺部CT扫描中的新COVID-19变种
Shervan Fekri-Ershad1,2, Khalegh Behrouz Dehkordi1
1Department of Computer Engineering, Na.C., Islamic Azad University, Najafabad 8514143131, Iran.
概括
一个新的深度学习模型使用肺部CT扫描和先进的纹理分析准确检测COVID-19变体. 这种自动诊断方法在识别各种病毒菌株方面表现出卓越的性能.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 随着COVID-19的流行,需要准确和快速的诊断,复杂的症状重叠与常见的呼吸系统疾病.
- 新出现的SARS-CoV-2变种构成了持续的全球健康威胁,强调了对先进诊断工具的需求.
- 肺部计算机断层扫描 (CT) 分析是开发自动化COVID-19检测方法的关键领域.
研究的目的:
- 开发一种新的深度学习框架,用于自动检测各种COVID-19变体.
- 通过将先进的纹理特征提取与肺部CT扫描中的空间信息分析相结合,提高诊断准确性.
- 为 COVID-19 变种创建一个计算高效和高度准确的诊断工具.
主要方法:
- 设计了一个双通道卷积神经网络 (CNN),用于纹理和空间特征处理的单独通道.
- 纹理模式的动态学习被采用,利用一个修改的本地二进制模式 (LBP) 技术来提取特征.
- 使用Adam优化器和焦点损失函数进行超参数优化,以平衡精度和效率.
主要成果:
- 拟议的深度学习模型在COVID-349和意大利COVID-Set基准数据集上进行了评估.
- 该方法表现出高诊断准确度,在各自的数据集上达到94.63%和95.47%.
- 该方法在精度,回忆和各种COVID-19变种的整体诊断可靠性方面超过了现有方法.
结论:
- 开发的深度学习方法提供了一种高度准确和可靠的方法,用于从肺部CT扫描中检测各种COVID-19变体.
- 这种自动诊断系统在传统方法上取得了显著的进步,特别是在区分类似的呼吸道疾病方面.
- 这些发现强调了人工智能驱动的医学图像分析在打击当前和未来传染病威胁方面的潜力.
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