Hybrid classical–quantum transfer learning for cardiomegaly detection in Chest X-rays

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dc.contributor.authorDecoodt, Pierre
dc.contributor.authorLiang, Tan Jun
dc.contributor.authorBopardikar, Sohan
dc.contributor.authorSanthanam, Hemavathi
dc.contributor.authorEyembe, Alfaxad
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorSierra-Sosa, Daniel
dc.date.accessioned2026-02-25T12:12:39Z
dc.date.available2026-02-25T12:12:39Z
dc.date.issued2023-07-25
dc.date.updated2026-02-25T12:12:39Z
dc.description.abstractCardiovascular diseases are among the major health problems that are likely to benefit from promising developments in quantum machine learning for medical imaging. The chest X-ray (CXR), a widely used modality, can reveal cardiomegaly, even when performed primarily for a non-cardiological indication. Based on pre-trained DenseNet-121, we designed hybrid classical–quantum (CQ) transfer learning models to detect cardiomegaly in CXRs. Using Qiskit and PennyLane, we integrated a parameterized quantum circuit into a classic network implemented in PyTorch. We mined the CheXpert public repository to create a balanced dataset with 2436 posteroanterior CXRs from different patients distributed between cardiomegaly and the control. Using k-fold cross-validation, the CQ models were trained using a state vector simulator. The normalized global effective dimension allowed us to compare the trainability in the CQ models run on Qiskit. For prediction, ROC AUC scores up to 0.93 and accuracies up to 0.87 were achieved for several CQ models, rivaling the classical–classical (CC) model used as a reference. A trustworthy Grad-CAM++ heatmap with a hot zone covering the heart was visualized more often with the QC option than that with the CC option (94% vs. 61%, p < 0.001), which may boost the rate of acceptance by health professionals.en
dc.identifier.citationDecoodt, P., Liang, T. J., Bopardikar, S., Santhanam, H., Eyembe, A., Garcia-Zapirain, B., & Sierra-Sosa, D. (2023). Hybrid classical–quantum transfer learning for cardiomegaly detection in Chest X-rays. Journal of Imaging, 9(7). https://doi.org/10.3390/JIMAGING9070128
dc.identifier.doi10.3390/JIMAGING9070128
dc.identifier.eissn2313-433X
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5240
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rights© 2023 by the authors
dc.subject.otherCardiomegaly
dc.subject.otherCardiovascular diseases
dc.subject.otherChest X-ray
dc.subject.otherDiagnosis
dc.subject.otherHeart failure
dc.subject.otherMachine learning
dc.subject.otherMedical imaging
dc.subject.otherQuantum computing
dc.subject.otherTransfer learning
dc.subject.otherVisualization
dc.titleHybrid classical–quantum transfer learning for cardiomegaly detection in Chest X-raysen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue7
oaire.citation.titleJournal of Imaging
oaire.citation.volume9
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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