Hybridized Deep Feature Learning for Clinical-Level Differentiation of Pulmonary Infections from Chest Radiographs

Abstract

Pulmonary infections, especially pneumonia constitute a major worldwide health burden and need early and accurate diagnosis in order to minimize disease severity and death. Chest radiographs are regularly performed for screening of pulmonary infections; however, the interpretation of the radiographs by manual techniques is time consuming and subject to inter-observer variability, and this has motivated the need for automated and reliable diagnostic systems. In the current work, a hybrid deep feature learning framework for clinical-level differentiation of pulmonary infections from the chest X-ray images is proposed by effectively incorporating local and global feature representations. The proposed model consists of EfficientNet-B3 for extracting deep spatial features and a Vision Transformer to extract long-range contextual dependencies, a feature fusion strategy towards better representation learning. The framework is tested on a publicly available chest X-ray dataset of Normal and Pneumonia classes. Experimental results show the high classification accuracy of the proposed hybrid model is 98.3% with high precision, recall, F1-score, and AUC performance compared with traditional CNN, ResNet, auto-encoder and transformer-based model. The results underscore the clinical reliability, good generalization capacity and possible applicability of the proposed framework to automated pulmonary infection screening and decision support systems.