首页|Multi-graph Networks with Graph Pooling for COVID-19 Diagnosis

Multi-graph Networks with Graph Pooling for COVID-19 Diagnosis

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Convolutional Neural Networks(CNNs)have shown remarkable capabilities in extracting local features from images,yet they often overlook the underlying relationships between pixels.To address this limitation,previous approaches have attempted to combine CNNs with Graph Convolutional Networks(GCNs)to capture global features.However,these approaches typically neglect the topological structure information of the graph during the global feature extraction stage.This paper proposes a novel end-to-end hybrid architecture called the Multi-Graph Pooling Network(MGPN),which is designed explicitly for chest X-ray image classification.Our approach sequentially combines CNNs and GCNs,enabling the learning of both local and global features from individual images.Recognizing that different nodes contribute dif-ferently to the final graph representation,we introduce an NI-GTP module to enhance the extraction of ultimate global features.Additionally,we introduce a G-LFF module to fuse the local and global features effectively.

Convolutional neural networksGraph convolutional networksGraph poolingCOVID-19

Chaosheng Tang、Wenle Xu、Junding Sun、Shuihua Wang、Yudong Zhang、Juan Manuel Górriz

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School of Computer Science and Technology,Henan Polytechnic University,HenanJiaozuo 454003,People's Republic of China

Department of Biological Sciences,School of Science,Xi'an Jiaotong Liverpool University,Suzhou 215123,Jiangsu,China

School of Computing and Mathematical Sciences,University of Leicester,Leicester LE1 7RH,UK

Department of Information Systems,Faculty of Computing and Information Technology,King Abdulaziz University,21589 Jeddah,Saudi Arabia

Department of Signal Theory,Networking and Communications,University of Granada,52005 Granada,Spain

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2024

10.1007/s42235-024-00600-9

仿生工程学报(英文版)
吉林大学

仿生工程学报(英文版)

CSTPCDEI
影响因子：0.837
ISSN：1672-6529
年,卷(期)：2024.21(6)