Abstract :

The Rapid Digital Transformation Of Healthcare Has Resulted In Unprecedented Growth In Medical Data Generated From Electronic Health Records (EHRs), Medical Imaging Systems, Laboratory Platforms, Genomic Sequencing, Wearable Devices, Internet Of Medical Things (IoMT) Sensors, Telemedicine Applications, Clinical Notes, And Remote Patient-monitoring Services. The Large Volume, Velocity, Variety, And Complexity Of These Datasets Create Significant Computational Challenges For Conventional Centralized Healthcare Analytics And Traditional Machine Learning Systems. This Research Proposes A Comprehensive Modern Deep Learning Framework For Efficient Processing Of Big Medical Data In Cloud Environments That Integrates Heterogeneous Medical Data Acquisition, Distributed Preprocessing, Cloudnative Storage, Scalable Deep Learning, Intelligent Workload Orchestration, Model Serving, Security, And Continuous Monitoring Within A Unified Architecture. The Framework Acquires Structured, Semi-structured, Unstructured, Image, Signal, Genomic, And Streaming Medical Information And Processes It Through Distributed Data-cleaning, Normalization, Anonymization, Feature Transformation, Medical-image Preprocessing, And Data-quality Validation Mechanisms. Modern Deep Learning Models, Including Convolutional Neural Networks, Long Short-Term Memory Networks, Transformers, Autoencoders, And Multimodal Fusion Architectures, Are Dynamically Selected According To Data Modality And Analytical Requirements. Cloud-based GPU And Accelerator Resources Support Distributed Training, Parallel Inference, Elastic Scaling, Containerized Deployment, And High-throughput Medical Data Processing. A Cloud Orchestration Mechanism Continuously Evaluates Workload Size, Model Complexity, Resource Availability, Latency Requirements, And Healthcare Service Priority To Allocate Computational Resources Efficiently. The Architecture Consists Of Five Interconnected Layers: Medical Big Data Acquisition Layer, Distributed Preprocessing And Cloud Data Engineering Layer, Modern Deep Learning And Intelligent Analytics Layer, Cloud Resource Orchestration And Model Serving Layer, And Healthcare Application, Security And Monitoring Layer. Illustrative Conceptual Evaluation Demonstrates Improved Processing Accuracy, Precision, Recall, F1-score, Cloud-processing Efficiency, And Reduced Analytical Response Time Compared With Conventional Centralized Processing, Traditional Cloud Machine Learning, And Basic Distributed Deep Learning Approaches. The Proposed Framework Provides A Scalable Foundation For Medical Imaging Analytics, Disease Prediction, Clinical Decision Support, Patient-risk Assessment, Remote Monitoring, Personalized Medicine, And Large-scale Healthcare Intelligence.

Published:

09-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

356-366

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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