Problem
Chest X-ray pneumonia detection requires models that are not only accurate, but also reliable enough for high-stakes clinical decision support.
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IEEE BECITHCON 2025
Risk-Calibrated YOLO: A Lightweight Pipeline for Reliable Pneumonia Detection in Chest X-Rays
A medical computer-vision paper by H. M. Ricon, T. Alam, M. T. Rahat, and K. Nur on reliable pneumonia detection from chest X-rays.
Why It Matters
In medical AI, overconfident mistakes can be more dangerous than ordinary errors. A useful detector should expose calibrated risk and uncertainty, not just a label.
Core Idea
The paper frames pneumonia detection as a lightweight YOLO-style pipeline with risk calibration to improve reliability in chest X-ray analysis.
Main Result
The work appears in the Scholar publication list as a 2025 IEEE conference paper in biomedical engineering, computer science, and information technology.
What Was Difficult
Medical imaging models must balance lightweight deployment, detection performance, calibration, and clinical interpretability.
Resources
Open Directions
- Which calibration metrics best reflect clinical reliability?
- Can lightweight detectors remain robust across hospitals and imaging devices?
- How should risk-aware outputs be shown to clinicians?