Performance Analysis of Task Offloading in Mobile Edge Cloud Computing for Brain Tumor Classification Using Deep Learning

Abstract

The increasing prevalence of brain tumors necessitates accurate and efficient methods for their identification and classification. While deep learning (DL) models have shown promise in this domain, their computational demands pose challenges when deploying them on resource-constrained mobile devices. This paper investigates the potential of Mobile Edge Computing (MEC) and Task Offloading to improve the performance of DL models for brain tumor classification. A comprehensive framework was developed, considering the computational capabilities of mobile devices and edge servers, as well as communication costs associated with task offloading. Various factors influencing task offloading decisions were analyzed, including model size, available resources, and network conditions. Results demonstrate that task offloading effectively reduces the time and energy required to process DL models for brain tumor classification, while maintaining accuracy. The study emphasizes the need to balance computation and communication costs when deciding on task offloading. These findings have significant implications for the development of efficient mobile edge computing systems for medical applications. Leveraging MEC and Task Offloading enables healthcare professionals to utilize DL models for brain tumor classification on resource-constrained mobile devices, ensuring accurate and timely diagnoses. These technological advancements pave the way for more accessible and efficient medical solutions in the future.

Keywords

• Brain Tumor Classification
• Deep Learning Models
• Mobile Edge Computing
• Task Offloading
• Resource-Constrained Mobile Devices