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QNMF: A quantum neural network based multimodal fusion system for intelligent diagnosis
Nanjing University of Information Science and Technology, Nanjing, China; Nanjing University of Information Science and Technology, Nanjing, China.
Nanjing University of Information Science and Technology, Nanjing, China; Nanjing University of Information Science and Technology, Nanjing, China.
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-2851-4260
2023 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 100, article id 101913Article in journal (Refereed) Published
Abstract [en]

The Internet of Medical Things (IoMT) has emerged as a significant research area in the medical field, enabling the transmission of various types of data to the cloud for analysis and diagnosis. Fusing data from multiple modalities can enhance accuracy but requires substantial computing power. Theoretically, quantum computers can rapidly process large volumes of high-dimensional medical data. Despite accelerated developments in quantum computing, research on quantum machine learning (QML) for multimodal data processing remains limited. Considering these factors, this paper presents a quantum neural network-based multimodal fusion system for intelligent diagnosis (QNMF) that can process multimodal medical data transmitted by IoMT devices, fuse data from different modalities, and improve the performance of intelligent diagnosis. This system employs a quantum convolutional neural network (QCNN) to efficiently extract features from medical images. These QCNN-based features are then fused with other modality features (such as blood test results or breast cell slices), and used to train an effective variational quantum classifier (VQC) for intelligent diagnosis. The experimental results demonstrate that a QCNN can effectively extract image data features. Furthermore, QNMF achieved an accuracy of 97.07% and 97.61% on breast cancer diagnosis and Covid-19 diagnosis experiments, respectively. In addition, the QNMF exhibits strong quantum noise robustness. © 2023 Elsevier B.V.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2023. Vol. 100, article id 101913
Keywords [en]
Internet of medical things, Multimodal fusion, Quantum neural network, Smart healthcare
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hh:diva-51409DOI: 10.1016/j.inffus.2023.101913ISI: 001048902500001Scopus ID: 2-s2.0-85165445106OAI: oai:DiVA.org:hh-51409DiVA, id: diva2:1788112
Note

Funding: The National Natural Science Foundation of China (No. 61373131, 62071240), PAPD and CICAEET funds.

Available from: 2023-08-15 Created: 2023-08-15 Last updated: 2023-10-05Bibliographically approved

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Tiwari, Prayag

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