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一个基于深度学习的新框架,用于从X射线图像中诊断COVID-19

SeyyedMohammad JavadiMoghaddam¹

¹Computer Engineering, Bozorgmehr University of Qaenat, Qaen, South Khorasan, Iran.

PeerJ. Computer science

|June 22, 2023

概括

一个新的深度学习框架从X射线图像中准确地检测到COVID-19,达到99.81%的准确性. 该系统帮助放射科医生进行初始查,这对于控制疾病传播至关重要.

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:

冠状病毒疾病 (COVID-19) 构成了全球健康的重大威胁,需要快速准确的诊断方法.
尽管存在人为错误和时间限制等挑战,但X射线成像仍然是在资源有限的环境中检测COVID-19的重要工具.
深度学习 (DL) 为自动化COVID-19诊断提供了有希望的解决方案,解决了对疾病控制准确性的关键需求.

研究的目的:

开发一种新的深度神经网络框架,从医学X射线图像中高精度,在线识别COVID-19.
提高COVID-19诊断的准确性和效率,特别是在获得先进诊断工具包有限的地区.

主要方法:

修改后的DenseNet-121架构被用于神经网络.
图像数据使用专用加载器进行处理,具有损失函数以最大限度地减少预测错误.
一个加权的随机抽样器平衡了训练阶段,一个优化器改进了神经网络属性.

主要成果:

拟议的框架表现出高诊断性能,在涉及各种类型肺炎的实验中达到99.81%的准确性.
该系统在区分COVID-19病例与其他形式的肺炎方面被证明是有效的.

结论:

开发的深度学习框架为在线医学图像识别提供了高度准确的方法.
该系统可以作为放射科医生的宝贵辅助工具,提高初始COVID-19查的准确性.

关键词:

发现COVID-19的检测方法深度学习模型深度学习模型在DenseNet中,使用的是DenseNet.损失函数是一个损失函数.一些X射线图像.

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