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dc.contributor.authorParisi, Luca
dc.contributor.authorNeagu, Daniel
dc.contributor.authorMa, R.
dc.contributor.authorCampean, Felician
dc.date.accessioned2021-09-17T14:56:28Z
dc.date.accessioned2021-10-11T08:43:46Z
dc.date.available2021-09-17T14:56:28Z
dc.date.available2021-10-11T08:43:46Z
dc.date.issued2022-01
dc.identifier.citationParisi L, Neagu D, Ma R et al (2022) Quantum ReLU activation for Convolutional Neural Networks to improve diagnosis of Parkinson’s disease and COVID-19. Expert Systems with Applications. 187: 115892.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18613
dc.descriptionYesen_US
dc.description.abstractThis study introduces a quantum-inspired computational paradigm to address the unresolved problem of Convolutional Neural Networks (CNNs) using the Rectified Linear Unit (ReLU) activation function (AF), i.e., the ‘dying ReLU’. This problem impacts the accuracy and the reliability in image classification tasks for critical applications, such as in healthcare. The proposed approach builds on the classical ReLU and Leaky ReLU, applying the quantum principles of entanglement and superposition at a computational level to derive two novel AFs, respectively the ‘Quantum ReLU’ (QReLU) and the ‘modified-QReLU’ (m-QReLU). The proposed AFs were validated when coupled with a CNN using seven image datasets on classification tasks involving the detection of COVID-19 and Parkinson’s Disease (PD). The out-of-sample/test classification accuracy and reliability (precision, recall and F1-score) of the CNN were compared against those of the same classifier when using nine classical AFs, including ReLU-based variations. Findings indicate higher accuracy and reliability for the CNN when using either QReLU or m-QReLU on five of the seven datasets evaluated. Whilst retaining the best classification accuracy and reliability for handwritten digits recognition on the MNIST dataset (ACC = 99%, F1-score = 99%), avoiding the ‘dying ReLU’ problem via the proposed quantum AFs improved recognition of PD-related patterns from spiral drawings with the QReLU especially, which achieved the highest classification accuracy and reliability (ACC = 92%, F1-score = 93%). Therefore, with these increased accuracy and reliability, QReLU and m-QReLU can aid critical image classification tasks, such as diagnoses of COVID-19 and PD.en_US
dc.description.sponsorshipThe authors declare that this was the result of a HEIF 2020 University of Bradford COVID-19 response-funded project ‘Quantum ReLU-based COVID-19 Detector: A Quantum Activation Function for Deep Learning to Improve Diagnostics and Prognostics of COVID-19 from Non-ionising Medical Imaging’. However, the funding source was not involved in conducting the study and/or preparing the article.en_US
dc.language.isoenen_US
dc.publisherElsevier
dc.relation.isreferencedbyhttps://doi.org/10.1016/j.eswa.2021.115892en_US
dc.rights© 2021 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.en_US
dc.subjectActivation functionsen_US
dc.subjectReLUen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectDecision supporten_US
dc.subjectCOVID-19en_US
dc.subjectParkinson’s diseaseen_US
dc.titleQuantum ReLU activation for Convolutional Neural Networks to improve diagnosis of Parkinson’s disease and COVID-19en_US
dc.status.refereedYesen_US
dc.date.Accepted2021-09-06
dc.date.application2021-09-14
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
dc.date.updated2021-09-17T14:56:30Z
refterms.dateFOA2021-10-11T08:51:48Z
dc.openaccess.statusGreenen_US


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