Performance Analysis of Task Offloading in Mobile Edge Cloud Computing for Brain Tumor Classification Using Deep Learning
DOI:
https://doi.org/10.37385/jaets.v4i2.2164Keywords:
Brain Tumor Classification, Deep Learning Models, Mobile Edge Computing, Task Offloading, Resource-Constrained Mobile DevicesAbstract
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.
Downloads
References
A. Sehgal, S. G. (2016). Automatic brain tumor segmentation and extraction in MR images. In Proc. Conf. Adv. Signal Process. (CASP), 104–107.
Agrawal., G. B. (2016). Multi stage classification and segmentation of brain tumor. In Proc. 3rd Int. Conf. Comput. Sustain. Global Develop. (INDIACom), 1628–1632.
Association., A. B. (2021). American Brain Tumor Association. Brain Tumor Facts and Figures. Retrieved from https://www.abta.org/brain-tumor-facts-and-statistics/.
Bhima K, J. (2017). New Method for Automatic Detection of Brain Tumor in Multimodal Brain Magnetic Resonance Images. International Journal of Computer Engineering In Research Trends, 26-29.
Bukhari, M. M. (2022). An Intelligent Proposed Model for Task Offloading in Fog-Cloud Collaboration Using Logistics Regression. Wireless Communications and Mobile Computing, , 1-19.
Chai, R. S. (2020). Joint Task Offloading, CNN Layer Scheduling, and Resource Allocation in Cooperative Computing System. IEEE Systems Journal, 5350–5361.
D.Sreedevi, K. (2019). A Review on Typical and Modern Brain MRI Image Segmentation Methods and Challenges. International Journal of Computer Engineering in Research Trends, 322-329.
Dand?l, E. Ç. (2015). Computer-aided diagnosis of malign and benign brain tumors on MR images. In ICT Innovations: Advances in Intelligent Systems and Computing, 157–166.
Gangolu Yedukondalu, S. K. (2022). MOCF: A Multi-Objective Clustering Framework using an Improved Particle Swarm Optimization Algorithm. International Journal on Recent and Innovation Trends in Computing and Communication, 143-154.
Gumaei, A. H. (2019). A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification. IEEE Access, 7, , 36266–36273.
hen, M. &. (2018). Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network. IEEE Journal on Selected Areas in Communications, 587–597.
iu, T. C. (2020). Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges. IEEE Communications Surveys & Tutorials, , 2462-2488.
K. Samunnisa, B. M. (2016). Privacy-Preserving Scalar Product Computation over Personal Health Records. International Journal of Computer Engineering In Research Trends, 42-46.
Kabiri., A. A. (2016). Multispectral MRI image segmentation using Markov random field model. Signal, Image Video Process., 251–258.
Karpathy, A. (2016). CS231n: Convolutional Neural Networks for Visual Recognition. Stanford University.
Litjens, G. K. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88.
Liu, C.-F. B. (2019). Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing. IEEE Transactions on Communications, 4132–4150.
M, P. &. (2023). ICN Scheme and Proxy re-encryption for Privacy Data Sharing on the Block Chain. International Journal of Computer Engineering in Research Trends, 172–176.
Madapudi, R. K. (2012). Change requests artifacts to assess impact on structural design of SDLC phases. Int’l J. Computer Applications, 21-26.
Mao, Y. Z. (2016). Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices. IEEE Journal on Selected Areas in Communications, 3590–3605.
Meng, L. W. (2022). Task Offloading Optimization Mechanism Based on Deep Neural Network in Spatio-temporal Crowdsourcing.
Mir., S. A. (2016). Statistical textural feature and deformable model based MR brain tumor segmentation. In Proc. 6th Int. Conf. Adv. Comput., Commun. Inform. (ICACCI), 657–663.
Pereira, S. P. (2016). Brain tumor segmentation using convolutional neural networks in MRI images. EEE Transactions on Medical Imaging, 35(5), 1240–1251.
Pydala, B. K. (2023). Smart_Eye: A Navigation and Obstacle Detection for Visually Impaired People through Smart App. Journal of Applied Engineering and Technological Science (JAETS), 992-1011.
Qi, P. (2022). Task Offloading and Scheduling Strategy for Intelligent Prosthesis in Mobile Edge Computing Environment. . Wireless Communications and Mobile Computing,, 1-13.
Ramana, K. A. (2022). Leaf disease classification in smart agriculture using deep neural network architecture and IoT. Journal of Circuits, Systems and Computers, 2240004.
Russakovsky, O. D.-F. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211–252.
Sze, V. C.-H.-J. (2017). Efficient Processing of Deep Neural Networks: A Tutorial and Survey. IEEE, 2295-2329.
Tajbakhsh, N. S. (2016). Convolutional Neural Networks for Medical Image Analysis. Full Training or Fine Tuning? IEEE Transactions on Medical Imaging, 35(5), 1299–1312.
Thinh, T. Q. (2017). Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling. IEEE Transactions on Communications, 1–1.
Tran, T. X. (2019). Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks. IEEE Transactions on Vehicular Technology, 856–868.
Xiong, J. G. (2023). Multi-agent deep reinforcement learning for task offloading in group distributed manufacturing systems. Engineering Applications of Artificial Intelligence,.
Yang, S. L. (2022). Deep Learning-Based Dynamic Computation Task Offloading for Mobile Edge Computing Networks. Sensors, 4088.
Yu, Z. G. (2020). Joint Task Offloading and Resource Allocation in UAV-Enabled Mobile Edge Computing. IEEE Internet of Things Journal, 3147–3159.
Zhang, C. Z. (2022). A multi-access edge computing enabled framework for the construction of a knowledge-sharing intelligent machine tool swarm in Industry 4.0. Journal of Manufacturing Systems, 64, , 246-258.
Zhang, P., Su, Y., Li, B., Liu, L., Wang, C., Zhang, W., et al. (2023). Deep Reinforcement Learning Based Computation Offloading in UAV-Assisted Edge Computing. Drones, 213.
Zhao, G. X. (2020). Offloading Dependent Tasks in Mobile Edge Computing with Service Caching. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications.
CITEDNESS IN SCOPUS
CITEDNESS IN WOS
