FLIFuse: a lightweight parallel multi-branch network for pneumonia pathogen identification in chest x-rays

Introduction Pneumonia is one of the deadliest lower respiratory tract infections worldwide, and rapidly distinguishing bacterial pneumonia, viral pneumonia, and normal chest radiographs is crucial for guiding precise medication and avoiding misdiagnosis. However, the imaging manifestations of bacterial and viral pneumonia on chest X-rays often overlap, and some normal chest X-rays may also show atypical changes. Clinical differentiation relies heavily on the expertise of senior physicians. In primary care or emergency settings where experienced radiologists are scarce, this can readily lead to diagnostic delays and antibiotic misuse, further exacerbating the risk of antimicrobial resistance and severe outcomes. Methods To address this challenge, this study proposes a lightweight network named FLIFuse. By adopting a parallel multi-branch architecture, the model thoroughly mines discriminative imaging features, substantially reducing computational overhead while maintaining high classification accuracy. This provides a technical solution that balances accuracy and efficiency for medical image analysis in resource-constrained environments. FLIFuse consists of three heterogeneous branches: an ExtraDW module that enhances local texture details, a Conv2Next module that captures global semantic structures, and an FLI Block that fuses multi-granularity information across different receptive fields to strengthen the response in critical regions. Results The model occupies only 33.43 M parameters and incurs a computational cost of 6.61 GFLOPs. On the public ChestXRay2017 dataset, FLIFuse achieves a three-class classification accuracy of 91.4% for normal, bacterial pneumonia, and viral pneumonia, outperforming several current state-of-the-art models and attaining extremely low resource consumption while ensuring diagnostic accuracy. In cross-domain tests on an external COVID-19 chest X-ray dataset and the Nerthus endoscopic image dataset, the model also exhibits stable transferability, demonstrating its potential for cross-domain adaptation across different medical imaging tasks. Discussion These results demonstrate that FLIFuse strikes a favorable balance between diagnostic accuracy and computational efficiency, providing an efficient and lightweight model option for computer-aided interpretation of pneumonia imaging, and showing potential for translation into clinical auxiliary diagnosis.