FSGCN: Feature-connected graph neural network for fine-grained silicosis classification

Abstract

Fine-grained classification of pulmonary diseases remains a challenging task due to subtle inter-class variations and overlapping visual patterns in Chest X-ray imaging. In this work, we propose FSGCN (Fine-grained Silicosis Graph-based Classification Network), a novel hybrid architecture that combines convolutional representation learning with relational reasoning via a graph transformer network (GTN). Specifically, image features extracted from a deep encoder are treated as nodesin a fully connected graph, where edge relationships are dynamically learned to capture semantic correlations among instances within a batch. This graph-based interaction enables the model to better distinguish silicosis from other visually similar pulmonary conditions, such as viral and bacterial pneumonia. We evaluate the proposed approach on SVBCX, a curated multiclass chest X-ray dataset collected and annotated by our research team, comprising four categories: Normal, Silicosis, Viral, andBacterial. The experimental results demonstrate the superiority of our method over existing baselines.
On average, FSGCN achieves an absolute improvement of +1.51% in accuracy and +1.85% in F1-score compared to state-of-the-art baseline models. In addition, qualitative visualizations further confirm the effectiveness of the proposed model in enhancing class discriminability. These results highlight the importance of modeling inter-instance relationships for fine-grained disease classification in medical imaging.