Clinically Constrained Reinforcement Learning for Optimized Adaptive Data Augmentation in Chest X-Ray Pneumonia Classification
Abstract
A clinically constrained reinforcement learning framework for chest X-ray pneumonia classification. Combines CNN-Transformer architecture with PPO-based adaptive augmentation, achieving 97.23% accuracy with excellent calibration (ECE = 0.016).
Fields of Study & Subjects
COVID-19 diagnosis using AI
Artificial Intelligence in Healthcare and Education
Radiomics and Machine Learning in Medical Imaging
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 14 January 2026