Dendritic Deep Residual Learning for <scp>COVID</scp>‐19 Prediction
Jiayi Li, Zhi-Peng Liu, Rong‐Long Wang, Shangce Gao
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. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Topics & Concepts
ResidualResidual neural networkDeep learningCoronavirus disease 2019 (COVID-19)Artificial intelligenceComputer scienceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)State (computer science)2019-20 coronavirus outbreakAlgorithmVirologyMedicineInfectious disease (medical specialty)PathologyDiseaseOutbreakCOVID-19 diagnosis using AIAnomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning