Multi-class medical image classification using DL continues to face major challenges, including managing multi-modal data, adapting to new tasks, handling distributed datasets, and operating under limited computational resources. Existing approaches fail to address these issues simultaneously, restricting the clinical scalability of AI in healthcare. To overcome these limitations, this paper introduces BigOrthoATD.Net, a unified, serverless, and decentralized learning framework that redefines scalability, adaptability, and efficiency in orthopedic image analysis. Designed to operate across distributed clinical nodes, BigOrthoATD.Net enables privacy-preserving knowledge fusion and multimodal integration across X-ray and CT imaging modalities. The framework supports progressive scalability for new tasks and institutions, achieving continual learning without retraining or performance degradation. Comprehensive experiments conducted across 13 simulated decentralized nodes and 50 orthopedic classes demonstrated that BigOrthoATD.Net achieved a state-of-the-art accuracy of 97.0%, outperforming swarm learning (70.8%) and centralized learning (84.8%), while federated learning failed to converge beyond moderate scale under identical resource-constrained conditions. BigOrthoATD.Net establishes a new benchmark for decentralized medical imaging by surpassing both centralized and decentralized frameworks in accuracy, scalability, and class diversity, while operating efficiently in low-resourced settings.
Alwzwazy et al. (Mon,) studied this question.