Self-FAGCFN: Graph-Convolution Fusion Network Based on Feature Fusion and Self-Supervised Feature Alignment for Pneumonia and Tuberculosis Diagnosis

Feature fusion is an important technique in medical image classification that can improve diagnostic accuracy by integrating complementary information from multiple sources. Recently, Deep Learning (DL) has been widely used in pulmonary disease diagnosis, such as pneumonia and tuberculosis. However, traditional feature fusion methods often suffer from feature disparity, information loss, redundancy, and increased complexity, hindering the further extension of DL algorithms. To solve this problem, we propose a Graph-Convolution Fusion Network with Self-Supervised Feature Alignment (Self-FAGCFN) to address the limitations of traditional feature fusion methods in deep learning-based medical image classification for respiratory diseases such as pneumonia and tuberculosis. The network integrates Convolutional Neural Networks (CNNs) for robust feature extraction from two-dimensional grid structures and Graph Convolutional Networks (GCNs) within a Graph Neural Network branch to capture features based on graph structure, focusing on significant node representations. Additionally, an Attention-Embedding Ensemble Block is included to capture critical features from GCN outputs. To ensure effective feature alignment between pre- and post-fusion stages, we introduce a feature alignment loss that minimizes disparities. Moreover, to address the limitations of proposed methods, such as inappropriate centroid discrepancies during feature alignment and class imbalance in the dataset, we develop a Feature-Centroid Fusion (FCF) strategy and a Multi-Level Feature-Centroid Update (MLFCU) algorithm, respectively. Extensive experiments on public datasets LungVision and Chest-Xray demonstrate that the Self-FAGCFN model significantly outperforms existing methods in diagnosing pneumonia and tuberculosis, highlighting its potential for practical medical applications.