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Cognitive Computation
Erschienen in: Cognitive Computation 5/2022

10.08.2021

Quantum Machine Learning Architecture for COVID-19 Classification Based on Synthetic Data Generation Using Conditional Adversarial Neural Network

verfasst von: Javaria Amin, Muhammad Sharif, Nadia Gul, Seifedine Kadry, Chinmay Chakraborty

Erschienen in: Cognitive Computation | Ausgabe 5/2022

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Abstract

Background

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.

Methods

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.

Result

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.

Conclusion

The proposed method achieved better results when compared to the latest published work in this domain.
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Metadaten
Titel
Quantum Machine Learning Architecture for COVID-19 Classification Based on Synthetic Data Generation Using Conditional Adversarial Neural Network
verfasst von
Javaria Amin
Muhammad Sharif
Nadia Gul
Seifedine Kadry
Chinmay Chakraborty
Publikationsdatum
10.08.2021
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 5/2022
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-021-09926-6

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