Hybrid Quantum Machine Learning Assisted Classification of COVID-19 from Computed Tomography Scans

• URL: http://arxiv.org/abs/2310.02748v1
• Date: Wed, 4 Oct 2023 11:37:58 GMT
• Title: Hybrid Quantum Machine Learning Assisted Classification of COVID-19 from Computed Tomography Scans
• Authors: Leo S\"unkel, Darya Martyniuk, Julia J. Reichwald, Andrei Morariu, Raja Havish Seggoju, Philipp Altmann, Christoph Roch, Adrian Paschke
• Abstract summary: We apply a hybrid quantum machine learning approach to a practically relevant problem with real world-data. More specifically, we classify large CT-scans of the lung into COVID-19, CAP, or Normal. We discuss quantum image embedding as well as hybrid quantum machine learning and evaluate several approaches to quantum transfer learning with various quantum circuits and embedding techniques.
• Score: 0.8098766536552447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Practical quantum computing (QC) is still in its infancy and problems considered are usually fairly small, especially in quantum machine learning when compared to its classical counterpart. Image processing applications in particular require models that are able to handle a large amount of features, and while classical approaches can easily tackle this, it is a major challenge and a cause for harsh restrictions in contemporary QC. In this paper, we apply a hybrid quantum machine learning approach to a practically relevant problem with real world-data. That is, we apply hybrid quantum transfer learning to an image processing task in the field of medical image processing. More specifically, we classify large CT-scans of the lung into COVID-19, CAP, or Normal. We discuss quantum image embedding as well as hybrid quantum machine learning and evaluate several approaches to quantum transfer learning with various quantum circuits and embedding techniques.

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