Next-Generation Healthcare Frameworks: Lightweight CNNs, Capsule Networks, and Blockchain Alternatives for Real-Time Pandemic Detection and Data Security

Abstract

Traditional healthcare systems have difficulties such as delayed diagnosis, resource constraints, and data security issues, particularly during pandemics. Lightweight CNNs, capsule networks, and DAG-based blockchain alternatives are all included in a next-generation healthcare system to improve diagnostic precision, scalability, and decentralized data security. With GANs creating synthetic datasets for training, this method uses DAGs for safe and scalable data sharing, lightweight CNNs for feature extraction, and capsule networks for spatial representation. The real-time performance and interoperability of a modular design are confirmed by measurements for accuracy, sensitivity, and latency. In terms of safe data sharing and real-time pandemic detection, the suggested system outperformed traditional techniques with 99.9% data integrity, 96.4% accuracy, 97.1% sensitivity, 23.3 ms latency, and 1200 TPS scalability. It is an efficient option for healthcare settings with limited resources and real-time demands because of its scalability, robust security, and excellent diagnostic precision.

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