부산대학교 도서관

The incorporation of distributed deep learning for medical image processing in cloud settings is the subject of this study. The findings demonstrate the high viability and significant performance advantages realized by cloud-based distributed systems, notably significant processing time savings, outstanding diagnostic accuracy, as well as improved scalability. The consequences for security and privacy have been discussed, with a focus on effective safeguards for private medical information. There is a void in the literature about resource and cost-effectiveness optimization tactics used in cloud-based systems. Future research must concentrate on resource optimization tactics for economic sustainability, study developing security risks and privacy techniques, and incorporate real-world implementations in order to improve this topic. This study informs the use of distributed deep learning in cloud-based medical image processing as well as adds to the body of knowledge in healthcare technology.