Asadoorian Nazeh, Yaraghi Shokufeh, Tahmasian Araeek
Department of Computer Engineering, Faculty of Engineering, Shahid Ashrafi Esfahani University, Isfahan, Iran.
PeerJ Comput Sci. 2024 Oct 18;10:e2343. doi: 10.7717/peerj-cs.2343. eCollection 2024.
The COVID-19 pandemic has had a significant influence on economies and healthcare systems around the globe. One of the most important strategies that has proven to be effective in limiting the disease and reducing its rapid spread is early detection and quick isolation of infections. Several diagnostic tools are currently being used for COVID-19 detection using computed tomography (CT) scan and chest X-ray (CXR) images.
In this study, a novel deep learning-based model is proposed for rapid detection of COVID-19 using CT-scan images. The model, called pre-trained quantum convolutional neural network (QCNN), seamlessly combines the strength of quantum computing with the feature extraction capabilities of a pre-trained convolutional neural network (CNN), particularly VGG16. By combining the robust feature learning of classical models with the complex data handling of quantum computing, the combination of QCNN and the pre-trained VGG16 model improves the accuracy of feature extraction and classification, which is the significance of the proposed model compared to classical and quantum-based models in previous works.
The QCNN model was tested on a SARS-CoV-2 CT dataset, initially without any pre-trained models and then with a variety of pre-trained models, such as ResNet50, ResNet18, VGG16, VGG19, and EfficientNetV2L. The results showed the VGG16 model performs the best. The proposed model achieved 96.78% accuracy, 0.9837 precision, 0.9528 recall, 0.9835 specificity, 0.9678 F1-Score and 0.1373 loss.
Our study presents pre-trained QCNN models as a viable technique for COVID-19 disease detection, showcasing their effectiveness in reaching higher accuracy and specificity. The current paper adds to the continuous efforts to utilize artificial intelligence to aid healthcare professionals in the diagnosis of COVID-19 patients.
新冠疫情对全球经济和医疗系统产生了重大影响。事实证明，限制疾病传播并减缓其快速扩散的最重要策略之一是早期检测和快速隔离感染者。目前，有几种诊断工具用于通过计算机断层扫描（CT）和胸部X光（CXR）图像检测新冠病毒。
在本研究中，提出了一种基于深度学习的新型模型，用于使用CT扫描图像快速检测新冠病毒。该模型称为预训练量子卷积神经网络（QCNN），它将量子计算的优势与预训练卷积神经网络（CNN），特别是VGG16的特征提取能力无缝结合。通过将经典模型强大的特征学习与量子计算复杂的数据处理相结合，QCNN与预训练的VGG16模型的组合提高了特征提取和分类的准确性，这是该模型与先前工作中的经典模型和基于量子的模型相比的意义所在。
QCNN模型在一个SARS-CoV-2 CT数据集上进行了测试，最初没有任何预训练模型，然后使用了各种预训练模型，如ResNet50、ResNet18、VGG16、VGG19和EfficientNetV2L。结果表明VGG16模型表现最佳。所提出的模型达到了96.78%的准确率、0.9837的精确率、0.9528的召回率、0.9835的特异性、0.9678的F1分数和0.1373的损失。
我们的研究提出预训练的QCNN模型作为一种可行的新冠病毒疾病检测技术，展示了其在实现更高准确性和特异性方面的有效性。本文进一步推动了利用人工智能辅助医疗专业人员诊断新冠患者的持续努力。
