Adaptive Region‐Aware Compression for Healthcare Applications in <scp>O‐RAN</scp>

ABSTRACT Open Radio Access Network (O‐RAN) fronthaul links face stringent bandwidth, latency, and computational constraints, which become particularly critical when transmitting high‐resolution medical images. This paper proposes an adaptive region‐aware image compression framework for healthcare imaging over O‐RAN that reduces fronthaul load while preserving diagnostically relevant information. Each image is partitioned into Region‐of‐Interest (ROI) and Non‐ROI areas and compressed using independent quantisation parameters. An optimisation model is formulated to minimise transmitted data size subject to ROI and Non‐ROI quality constraints, end‐to‐end latency bounds and computational limits at O‐RAN nodes. The framework is evaluated using two medical imaging datasets (chest X‐rays and bone fracture X‐rays), where empirical rate–distortion and quality models are derived and validated. Results demonstrate substantial fronthaul bandwidth reduction—achieving compression ratios up to 416:1—while maintaining ROI quality and diagnostic accuracy above 97%. These findings highlight the effectiveness of region‐aware optimisation for bandwidth‐efficient healthcare imaging in O‐RAN environments.