Artificial intelligence of medical things for disease detection using ensemble deep learning and attention mechanism

In this paper, we present a novel paradigm for disease detection. We build an artificial intelligence based system where various biomedical data are retrieved from distributed and homogeneous sensors. We use different deep learning architectures (VGG16, RESNET, and DenseNet) with ensemble learning a...

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Bibliographic Details
Published inExpert systems Vol. 41; no. 6
Main Authors Djenouri, Youcef, Belhadi, Asma, Yazidi, Anis, Srivastava, Gautam, Lin, Jerry Chun‐Wei
Format Journal Article
LanguageEnglish
Published Oxford Blackwell Publishing Ltd 01.06.2024
Subjects
agriculture
Artificial intelligence
attention mechanism
Biomedical data
Deep learning
Ensemble learning
Health care
Medical diagnosis
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Summary:In this paper, we present a novel paradigm for disease detection. We build an artificial intelligence based system where various biomedical data are retrieved from distributed and homogeneous sensors. We use different deep learning architectures (VGG16, RESNET, and DenseNet) with ensemble learning and attention mechanisms to study the interactions between different biomedical data to detect and diagnose diseases. We conduct extensive testing on biomedical data. The results show the benefits of using deep learning technologies in the field of artificial intelligence of medical things to diagnose diseases in the healthcare decision‐making process. For example, the disease detection rate using the proposed methodology achieves 92%, which is greatly improved compared to the higher‐level disease detection models.
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ISSN:0266-4720
1468-0394
DOI:10.1111/exsy.13093