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首页> 外文期刊>BMC Medical Informatics and Decision Making >Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients
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Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients

机译：用于更快的临床预后分析的量子算法：CT扫描图像的应用与实验研究Covid-19患者

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In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice. This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases. The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52?min, while on K80 GPU hardware it was 1?h 30?min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%. The results suggest that quantum neural networks outperform in COVID-19 traits’ classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices.
机译：在医学诊断和临床实践中，早期诊断疾病对于准确治疗至关重要，减少了医疗保健系统的压力。在医学成像研究中，图像处理技术倾向于对具有高精度的分析和解决疾病至关重要。本文通过仿真技术建立了一种新的图像分类和分割方法，在印度Covid-19患者的图像上进行了模拟技术，在医疗实践中引入了量子机器学习（QML）的使用。本研究建立了用于分类Covid-19的原型模型，将其与计算断层扫描（CT）图像中的非Covid肺炎信号进行比较。仿真工作评估了量子机器学习算法的使用，同时评估了图像分类问题的深度学习模型的功效，从而建立了在处理具有高偏差的复杂临床图像数据时提高预测率所需的性能质量。该研究考虑了利用量子神经网络（QNN）的新颖算法实现。所提出的模型表现出特定分类任务的传统深层学习模型。由于Quantum仿真效率和更快的趋同性能，因此对网络培训的优化问题进行了更快的趋同问题，表现明显是显而易见的。在量子优化硬件上观察到的模型运行时间为52？min，而在K80 GPU硬件上，它为1？H 30？分钟，适用于类似的样本大小。该模拟表明，QNN优于DNN，CNN，2D CNN的精度措施中的292％以上，平均召回约为97.7％。结果表明，Quantum神经网络在CoVID-19特征的分类任务中占此胜过，与深度学习W.R.T模型效能和培训时间相比。然而，需要进行进一步的研究来评估通过在医疗设备内集成模型来评估实现方案。

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