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

Students & Supervisors

Student Authors
Fariya Sultana Prity
Master of Science in Computer Science, FST
Kaushik Biswas
Master of Science in Computer Science, FST
Supervisors
Firoz Ahmed
Professor, Faculty, FST

Abstract

Pneumonia remains a significant global health challenge, causing substantial morbidity and mortality worldwide. Deep learning approaches show promise in automating pneumonia 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 convolutional layers and attention gating to focus on diagnostically relevant features. The expected outcomes include improved diagnostic accuracy, reduced false positives, and enhanced model interpretability

Keywords

deep learning pneumonia detection dualstream architecture attention mechanisms chest x-ray analysis

Publication Details

  • Type of Publication:
  • Conference Name: 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN)
  • Date of Conference: 31/07/2025 - 31/07/2025
  • Venue: Rangpur
  • Organizer: IEEE Photonics Society Bangladesh Chapter
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