Dendritic Deep Residual Learning for COVID-19 Prediction

Jiayi Li; Zhipeng Liu; Rong Long Wang; Shangce Gao

doi:10.1002/tee.23723

Dendritic Deep Residual Learning for COVID-19 Prediction

Jiayi Li, Zhipeng Liu, Rong Long Wang, Shangce Gao^*

^*Corresponding author for this work

Intellectual Information Engineering

Research output: Contribution to journal › Letter › peer-review

9 Scopus citations

Abstract

Deep residual network (ResNet), one of the mainstream deep learning models, has achieved groundbreaking results in various fields. However, all neurons used in ResNet are based on the McCulloch-Pitts model which has long been criticized for its oversimplified structure. Accordingly, this paper for the first time proposes a novel dendritic residual network by considering the powerful information processing capacity of dendrites in neurons. Experimental results based on the challenging COVID-19 prediction problem show the superiority of the proposed method in comparison with other state-of-the-art ones.

Original language	English
Pages (from-to)	297-299
Number of pages	3
Journal	IEEJ Transactions on Electrical and Electronic Engineering
Volume	18
Issue number	2
DOIs	https://doi.org/10.1002/tee.23723
State	Published - 2023/02

Keywords

COVID-19
convolutional neural network
deep learning
dendritic neuron model

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1002/tee.23723

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@article{428de78e9b3a400bbb8b1c0631f5bf2a,

title = "Dendritic Deep Residual Learning for COVID-19 Prediction",

abstract = "Deep residual network (ResNet), one of the mainstream deep learning models, has achieved groundbreaking results in various fields. However, all neurons used in ResNet are based on the McCulloch-Pitts model which has long been criticized for its oversimplified structure. Accordingly, this paper for the first time proposes a novel dendritic residual network by considering the powerful information processing capacity of dendrites in neurons. Experimental results based on the challenging COVID-19 prediction problem show the superiority of the proposed method in comparison with other state-of-the-art ones.",

keywords = "COVID-19, convolutional neural network, deep learning, dendritic neuron model",

author = "Jiayi Li and Zhipeng Liu and Wang, {Rong Long} and Shangce Gao",

note = "Publisher Copyright: {\textcopyright} 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.",

year = "2023",

month = feb,

doi = "10.1002/tee.23723",

language = "英語",

volume = "18",

pages = "297--299",

journal = "IEEJ Transactions on Electrical and Electronic Engineering",

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number = "2",

}

TY - JOUR

T1 - Dendritic Deep Residual Learning for COVID-19 Prediction

AU - Li, Jiayi

AU - Liu, Zhipeng

AU - Wang, Rong Long

AU - Gao, Shangce

PY - 2023/2

Y1 - 2023/2

N2 - Deep residual network (ResNet), one of the mainstream deep learning models, has achieved groundbreaking results in various fields. However, all neurons used in ResNet are based on the McCulloch-Pitts model which has long been criticized for its oversimplified structure. Accordingly, this paper for the first time proposes a novel dendritic residual network by considering the powerful information processing capacity of dendrites in neurons. Experimental results based on the challenging COVID-19 prediction problem show the superiority of the proposed method in comparison with other state-of-the-art ones.

AB - Deep residual network (ResNet), one of the mainstream deep learning models, has achieved groundbreaking results in various fields. However, all neurons used in ResNet are based on the McCulloch-Pitts model which has long been criticized for its oversimplified structure. Accordingly, this paper for the first time proposes a novel dendritic residual network by considering the powerful information processing capacity of dendrites in neurons. Experimental results based on the challenging COVID-19 prediction problem show the superiority of the proposed method in comparison with other state-of-the-art ones.

KW - COVID-19

KW - convolutional neural network

KW - deep learning

KW - dendritic neuron model

UR - http://www.scopus.com/inward/record.url?scp=85139526757&partnerID=8YFLogxK

U2 - 10.1002/tee.23723

DO - 10.1002/tee.23723

M3 - Letter

AN - SCOPUS:85139526757

SN - 1931-4973

VL - 18

SP - 297

EP - 299

JO - IEEJ Transactions on Electrical and Electronic Engineering

JF - IEEJ Transactions on Electrical and Electronic Engineering

IS - 2

ER -

Dendritic Deep Residual Learning for COVID-19 Prediction

Abstract

Keywords

ASJC Scopus subject areas

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