A Framework for Cloud-Enhanced Deep Learning Models in Medical Image Analysis: Applications and Challenges

Abstract

Deep learning has revolutionized medical image analysis, offering advanced capabilities for disease detection, diagnosis, and treatment planning. However, the computational demands of training and deploying deep learning models, along with the need for extensive data management, pose significant challenges for traditional on-premise systems. Cloud computing provides scalable and flexible resources that can enhance deep learning models by offering high-performance computing, storage, and integrated services. This paper presents a comprehensive framework for integrating cloud-enhanced deep learning models in medical image analysis. We explore various applications, including automated diagnosis, image segmentation, and disease progression monitoring. Additionally, we address challenges related to data security, model deployment, and latency, and propose solutions for effective cloud integration. By leveraging cloud computing, medical image analysis can achieve improved scalability, efficiency, and accessibility, enhancing the quality of healthcare services.