A Principal Neighborhood Aggregation-Based Graph Convolutional Network for Pneumonia Detection

Pneumonia is one of the main causes of child mortality in the world and has been reported by the World Health Organization (WHO) to be the cause of one-third of child deaths in India. Designing an automated classification system to detect pneumonia has become a worthwhile research topic. Numerous deep learning models have attempted to detect pneumonia by applying convolutional neural networks (CNNs) to X-ray radiographs, as they are essentially images and have achieved great performances. However, they failed to capture higher-order feature information of all objects based on the X-ray images because the topology of the X-ray images’ dimensions does not always come with some spatially regular locality properties, which makes defining a spatial kernel filter in X-ray images non-trivial. This paper proposes a principal neighborhood aggregation-based graph convolutional network (PNA-GCN) for pneumonia detection. In PNA-GCN, we propose a new graph-based feature construction utilizing the transfer learning technique to extract features and then construct the graph from images. Then, we propose a graph convolutional network with principal neighborhood aggregation. We integrate multiple aggregation functions in a single layer with degree-scalers to capture more effective information in a single layer to exploit the underlying properties of the graph structure. The experimental results show that PNA-GCN can perform best in the pneumonia detection task on a real-world dataset against the state-of-the-art baseline methods.