Distributed Deep Learning Framework For Secure Medical Image Processing And Diagnostic Prediction In Smart Hospitals

Abstract

The fast development of artificial intelligence, distributed computing and medical imaging technologies have greatly changed smart healthcare systems to intelligent and automated clinical decision-making. Nevertheless, the centralized medical image processing systems are normally characterized by the high computation latency, low scalability, privacy, and poor coordination of diagnosis across the distributed healthcare systems. To enable scalable and privacy-preserving healthcare analytics the current paper proposes a Distributed Deep Learning Framework of Secure Medical Image Processing and Diagnostic Prediction in Smart Hospitals that uses edge intelligence, federated learning, distributed convolutional neural networks, and secure mechanisms of communicating medical data. The framework proposed integrates distributed image preprocessing, synchronized encrypted edge-cloud, adaptive diagnostic prediction, and explainable AI-based clinical interpretation to enhance diagnostic accuracy and minimal communication overhead and patient confidentiality. A distributed CNN-transformer hybrid model is employed to extract the features and predict the disease based on multimodal medical images such as MRI, CT and X-ray scans. The encryption of transfer by TLS is implemented and encrypted transfer and federated aggregation schemes are implemented to establish secure communication. Experimental analysis illustrates that in comparison to conventional centralized medical imaging systems, more prediction accuracy, better communication, scale, and reduction of latency are achieved. The suggested framework thus offers an effective and scalable smart healthcare framework to next-generation smart hospital settings.