Trustworthy pneumonia detection in chest X-ray imaging through attention-guided deep learning

Area of Science:

• Artificial Intelligence
• Medical Imaging
• Computational Neuroscience

Background:

• Pneumonia is a major global health concern, particularly for vulnerable populations.
• Manual interpretation of chest X-rays (CXRs) for pneumonia diagnosis faces challenges with accuracy and consistency.
• Automated diagnostic tools are needed to improve efficiency and reliability in clinical settings.

Purpose of the Study:

• To develop and evaluate a novel attention-guided deep learning framework for accurate and interpretable pneumonia detection from CXRs.
• To enhance diagnostic performance by integrating spatial, temporal, and biologically inspired processing.
• To improve the robustness and reliability of AI-based pneumonia diagnosis.

Main Methods:

• A deep learning framework incorporating convolutional neural networks (CNNs) for spatial features.
• Gated recurrent units (GRUs) for capturing temporal dependencies in image sequences.
• Spike-based neural processing for biological efficiency and noise tolerance.
• An attention mechanism to highlight clinically relevant regions for interpretability.

Main Results:

• The proposed model achieved a high diagnostic accuracy of 99.35% on a public CXR dataset.
• Excellent precision, recall, and F1-score were obtained, indicating strong diagnostic performance.
• The model demonstrated significant robustness against various image distortions, including noise and blur.

Conclusions:

• The attention-guided deep learning framework offers an effective, reliable, and transparent solution for pneumonia detection.
• This AI approach shows promise for clinical integration, especially in resource-limited healthcare environments.
• The biologically inspired components enhance model efficiency and resilience to image artifacts.