Research in Alzheimer’s disease demands extensive computational resources and efficient data management due to the large scale and complexity of multimodal biomedical data. This work proposes a multi-cloud integration strategy to enhance performance, scalability, and reliability by leveraging the complementary strengths of Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS). The proposed infrastructure supports scalable computing and storage through AWS, advanced analytics and machine learning workflows via Azure, and high-performance data processing using GCP. Core components include AWS S3 and Azure Blob Storage for resilient data storage, Apache Kafka for real-time data streaming, and Apache Spark for distributed processing. The system is optimized to improve model accuracy (94%) , reduce training time (128 minutes) , and enable efficient hyperparameter tuning and feature extraction for state-of-the-art deep learning models such as YOLOv4 and EfficientNetB2 used in Alzheimer’s detection and classification. Automated scaling and load balancing mechanisms ensure optimal resource utilization , low latency (24ms) , and high fault tolerance under varying workloads. Additionally, the architecture demonstrates strong scalability , allowing seamless expansion across cloud environments as data volume and computational demands increase. Performance monitoring and reliability are ensured through cloud-native observability tools such as Prometheus and Grafana, while robust security measures—including data encryption and fine-grained access control—protect sensitive medical data. Overall, the proposed multi-cloud approach significantly enhances the efficiency, reliability, and performance of Alzheimer’s disease assessment, providing a robust and scalable solution for advanced research, with further optimization and refinement identified as future work.
Kottapalli et al. (Sun,) studied this question.