On-Device Continual Learning with Dual-Stage Buffer and Dynamic Loss for Point-of-Care Pneumonia Diagnosis

• URL: http://arxiv.org/abs/2605.19201v1
• Date: Tue, 19 May 2026 00:00:19 GMT
• Title: On-Device Continual Learning with Dual-Stage Buffer and Dynamic Loss for Point-of-Care Pneumonia Diagnosis
• Abstract summary: We present PneumoNet, a domain-incremental learning method for point-of-care pneumonia diagnosis.<n>PneumoNet achieves 86.6% accuracy with 1.4% forgetting while being smaller and faster than existing baselines.
• Score: 0.29008108937701327
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep learning models detect pneumonia from chest X-rays with high accuracy, but the performance declines under domain shifts caused by differences in devices, patients, or institutions. We present PneumoNet, a domain-incremental learning method for point-of-care pneumonia diagnosis in resource-limited settings. PneumoNet combines a lightweight CNN for on-device prediction, a dual-stage balanced buffer for class-balanced replay, and a dynamic class-weighted loss to correct training-batch imbalances. Evaluated on a domain-shifted PneumoniaMNIST dataset simulating five realistic domain change scenarios, PneumoNet achieves 86.6% accuracy with 1.4% forgetting while being smaller and faster than existing baselines. These results highlight PneumoNet's potential to enable adaptive, privacy-preserving diagnostic AI directly on point-of-care medical devices in real-world and pandemic-ready healthcare.

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