Abstract: 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 differently 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.

Key words: Convolutional neural networks · Graph convolutional networks · Graph pooling · COVID-19