Cross Spectrum Aided Deep Feature Extraction Based Neuromuscular Disease Detection Framework

Roy, Sayanjit Singha; Samanta, Kaniska; Modak, Sudip; Chatterjee, Soumya; Bose, Rohit

doi:10.1109/lsens.2020.2992452

Cited by 8 publications

(1 citation statement)

References 7 publications

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“…In the new field of distributed deep learning, we also see examples that are being actively explored by exploring different types of deep learning models for disease detection based on biomedical data (Balachandar et al, 2020; Dwivedi et al, 2021; Ku et al, 2022; Roy et al, 2020; Xu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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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