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预训练的量子卷积神经网络用于使用计算机断层扫描图像进行COVID-19疾病分类

Nazeh Asadoorian¹, Shokufeh Yaraghi¹, Araeek Tahmasian¹

¹Department of Computer Engineering, Faculty of Engineering, Shahid Ashrafi Esfahani University, Isfahan, Iran.

PeerJ. Computer science

|December 9, 2024

概括

一个新的量子卷积神经网络 (QCNN) 模型与VGG16相结合,实现了从CT扫描中快速检测COVID-19的高精度. 这种人工智能方法提高了早期诊断,并帮助医疗保健专业人员有效识别疾病.

科学领域:

人工智能的人工智能
量子计算是一种量子计算.
医疗成像医学成像

背景情况:

由于COVID-19的流行,人们需要快速准确的诊断工具.
早期检测和隔离对于疾病控制至关重要.
计算机断层扫描 (CT) 扫描用于COVID-19诊断.

研究的目的:

提出一种新的深度学习模型,用于使用CT扫描快速检测COVID-19.
将量子计算与预训练的卷积神经网络集成在一起,以提高诊断准确度.
为了增强特征提取和分类用于COVID-19检测.

主要方法:

开发了一个预训练的量子卷积神经网络 (QCNN) 模型.
结合QCNN与预训练的卷积神经网络 (CNN) 模型,包括VGG16.
在SARS-CoV-2CT数据集上评估模型性能.

主要成果:

使用VGG16的QCNN模型表现出卓越的性能.
获得了96.78%的准确性,0.9837的精度,0.9528的回忆力和0.9835的特异性.
这款车型的F1-Score为0.9678,损失为0.1373.

结论:

预先训练的QCNN模型对于COVID-19检测是有效的.
拟议的模型为疾病诊断提供了高精度和特异性.
这种人工智能驱动的方法支持医疗保健专业人员诊断COVID-19患者.

关键词:

在 COVID-19 疫情中,深度学习是一种深度学习.预先训练的卷积神经网络.量子神经网络 (QNN) 是一个量子神经网络.

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