A collaborative inference strategy for medical image diagnosis in mobile edge computing environment.

ABSTRACT: The popularity and convenience of mobile medical image analysis and diagnosis in mobile edge computing (MEC) environments have greatly improved the efficiency and quality of healthcare services, necessitating the use of deep neural networks (DNNs) for image analysis. However, DNNs face performance and energy constraints when operating on the mobile side, and are limited by communication costs and privacy issues when operating on the edge side, and previous edge-end collaborative approaches have shown unstable performance and low search efficiency when exploring classification strategies. To address these issues, we propose a DNN edge-optimized collaborative inference strategy (MOCI) for medical image diagnosis, which optimizes data transfer and computation allocation by combining compressi

SUBMITTER: Zhang S 

PROVIDER: S-EPMC11935760 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature