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Published on: December 19, 2020
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COVID-19 diagnostic prediction on chest CT scan images using hybrid quantum-classical convolutional neural network
Haorong Zhao1, Xing Deng1, Haijian Shao1,2
1School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China.
Journal of Biomolecular Structure & Dynamics
|April 11, 2024
Summary
A new hybrid quantum-classical convolutional neural network (HQCNN) model accurately predicts COVID-19 from chest CT scans. This advanced AI approach offers high diagnostic accuracy, aiding in the precise identification of coronavirus disease 2019.
Area of Science:
- Artificial Intelligence
- Medical Imaging
- Quantum Computing
Background:
- Accurate COVID-19 diagnosis remains challenging despite extensive research.
- Chest computed tomography (CT) scans show promise for COVID-19 prediction.
- Existing diagnostic methods face limitations in precision and speed.
Purpose of the Study:
- To develop a novel model for predicting COVID-19 using chest CT scans.
- To leverage a hybrid quantum-classical approach for enhanced diagnostic capabilities.
- To improve the accuracy and reliability of COVID-19 identification.
Main Methods:
- A hybrid quantum-classical convolutional neural network (HQCNN) model was developed.
- The HQCNN model utilizes stochastic quantum circuits for image analysis.
- The model was trained and validated on two publicly available chest CT datasets.
Main Results:
- The HQCNN model achieved high diagnostic accuracies of 99.39% and 97.91% on the datasets.
- Precisions reached 99.19% and 98.52%, demonstrating strong predictive power.
- The model outperformed recently published methods in COVID-19 prediction accuracy.
Conclusions:
- The proposed HQCNN model offers a superior approach for predicting COVID-19 from chest CT scans.
- This quantum-classical hybrid model demonstrates significant potential for clinical application.
- The findings suggest a promising direction for AI-driven infectious disease diagnostics.
