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    Quantum ReLU activation for Convolutional Neural Networks to improve diagnosis of Parkinson’s disease and COVID-19

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    Accepted manuscript (999.5Kb)
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    Publication date
    2022-01
    Author
    Parisi, Luca
    Neagu, Daniel
    Ma, R.
    Campean, I. Felician
    Keyword
    Activation functions
    ReLU
    Convolutional Neural Network
    Decision support
    COVID-19
    Parkinson’s disease
    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.
    Peer-Reviewed
    Yes
    Open Access status
    Green
    
    Metadata
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    Abstract
    This 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.
    URI
    http://hdl.handle.net/10454/18613
    Version
    Accepted manuscript
    Citation
    Parisi 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.
    Link to publisher’s version
    https://doi.org/10.1016/j.eswa.2021.115892
    Type
    Article
    Collections
    Engineering and Informatics Publications

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