Quantum Machine Learning Architecture for COVID-19 Classification Based on Synthetic Data Generation Using Conditional Adversarial Neural Network.
Quantum Machine Learning Architecture for COVID-19 Classification Based on Synthetic Data Generation Using Conditional Adversarial Neural Network.
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DOI：
10.1007/s12559-021-09926-6
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发表时间：
2022
影响因子：
5.4
通讯作者：
Chakraborty C
中科院分区：
文献类型：
--
作者：
Amin J;Sharif M;Gul N;Kadry S;Chakraborty C
COVID-19是一种影响上呼吸道以及肺部的新型病毒。全球COVID-19大流行的规模、传播速度和死亡人数正在不断增加。计算机断层扫描（CT）扫描可以仔细用于检测和分析COVID-19病例。在CT图像/扫描中，在感染的早期阶段发现毛玻璃样阴影（GGO）。而在晚期，有一个叠加的肺实变。本研究探讨了量子机器学习（QML）和经典机器学习（CML）方法用于COVID-19图像分析。量子计算的最新发展促使研究人员使用QML探索新的想法和方法。所提出的方法包括两个阶段：在第一阶段，通过条件对抗网络（CGAN）生成合成CT图像，以增加数据集的大小，以进行准确的训练和测试。在第二阶段，对COVID-19/健康图像进行分类，其中提出了两个模型：CML和QML。该模型在POF医院数据集上获得了0.94的精确度（Pn）、0.94的准确度（Ac）、0.94的召回率（RI）和0.94的F1分数（Fe），而在UCSD-AI 4 H数据集上获得了0.96的Pn、0.96的Ac、0.95的RI和0.96的Fe。所提出的方法取得了更好的结果相比，在这一领域的最新发表的工作。
COVID-19 is a novel virus that affects the upper respiratory tract, as well as the lungs. The scale of the global COVID-19 pandemic, its spreading rate, and deaths are increasing regularly. Computed tomography (CT) scans can be used carefully to detect and analyze COVID-19 cases. In CT images/scans, ground-glass opacity (GGO) is found in the early stages of infection. While in later stages, there is a superimposed pulmonary consolidation. This research investigates the quantum machine learning (QML) and classical machine learning (CML) approaches for the analysis of COVID-19 images. The recent developments in quantum computing have led researchers to explore new ideas and approaches using QML. The proposed approach consists of two phases: in phase I, synthetic CT images are generated through the conditional adversarial network (CGAN) to increase the size of the dataset for accurate training and testing. In phase II, the classification of COVID-19/healthy images is performed, in which two models are proposed: CML and QML. The proposed model achieved 0.94 precision (Pn), 0.94 accuracy (Ac), 0.94 recall (Rl), and 0.94 F1-score (Fe) on POF Hospital dataset while 0.96 Pn, 0.96 Ac, 0.95 Rl, and 0.96 Fe on UCSD-AI4H dataset. The proposed method achieved better results when compared to the latest published work in this domain.
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期刊：
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影响因子：
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