Critical Region-Focused Neural Architecture for Improved Pneumonia Diagnosis in Chest X-Ray Images

Pneumonia remains a significant global health chal-lenge, causing substantial morbidity and mortality worldwide. Deep learning approaches show promise in automating pneu-monia detection from chest X-rays, but existing models struggle with feature localization and contextual understanding of critical regions. This research addresses these limitations by proposing a novel dual-stream attention-guided architecture that integrates complementary features from EfficientNetB3 and DenseNet121 backbones with dedicated spatial attention mechanisms. Our approach employs critical region detection through 2D convo-lutional layers and attention gating to focus on diagnostically relevant features. The expected outcomes include improved di-agnostic accuracy, reduced false positives, and enhanced model interpretability.