Enhancing Cloud Computing for Advanced Diagnosis and Treatment of Pancreatic Cancer

Abstract

Cloud computing has transformed import into healthcare in efficient storage, processing, and analysis of
massive medical data. This study parameterizes AI techniques from cloud-based improvement to advanced
diagnosis and treatment of pancreatic cancer. This research of deep learning model integration with cloud-based
infrastructure, like Deep Belief Networks (DBNs), aims to contribute to the better accuracy of diagnosis,
optimization of treatment planning, and real-time patient monitoring. Proposed modeling takes off with the secure
cloud for data collection, preprocessing techniques, and feature extraction for improved performance in
classification. Experimental evaluation reflects accuracy: 97.2%, precision: 95.9%, recall: 91.4%, and F1-score:
93.1%. The remaining 27% decrease in computational latency obtained by cloud-integrated AI frameworks also
meets data security requirements of HIPAA and GDPR compliance. The study highlights performance metrics
that provide robustness and reliability for the proposed approach. The findings demonstrate the promise of cloud-
based AI integration in enhancing patient outcomes and the optimization of healthcare workflows. Future models
undergo further enhancement using federated learning and edge computing to minimize latency while increasing
scalability. This leads towards AI-enhanced cloud for expeditious, safe, scalable cancer diagnostics, and treatment
methodologies.