Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)

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Shufflenet-Based Model for Fast and Accurate Brain MRI Detection and Classification

Authors
S. Arulselvi1, *, P. Kishore1, N. Aswinth1, A. RiyazAhamed1
1Department of Electronics and Communication Engineering, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India
*Corresponding author. Email: arulselvi.ece@bharathuniv.ac.in
Corresponding Author
S. Arulselvi
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-654-8_38How to use a DOI?
Keywords
ShuffleNet; Brain MRI; Detection; Classification; Real-time Processing
Abstract

The proposed article is design for us smart method of diagnosis to read the brain MRI scanned images and highlight the required details of the brain. The proposed article uses a system called shuffle net that help to spot the tumors and other problems with high speed and accuracy. Proposed design keeps calculations much faster but still precise using techniques like Max pooling and special CNN layers. In spite of the model is very small and efficient it can even run on devices with limited power such as mobile phones. Conventionally mobile phone usage is 100% possible with all nowadays which makes this possible with everyone. Finally, a connected decision layer makes system more optimal to identify the brain diseases. Test results show this method is accurate sensible and reliable that it could help doctors diagnose brain disorders quickly and with confidence. The solution enhances healthcare using powerful machine learning. The REMBRANDT database exposes ShuffleNet v2 performs well with ShuffleNet v2 is 99.2% specific, 98.0% sensitive, and 98.6% accurate.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
Series
Advances in Engineering Research
Publication Date
24 April 2026
ISBN
978-94-6239-654-8
ISSN
2352-5401
DOI
10.2991/978-94-6239-654-8_38How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - S. Arulselvi
AU  - P. Kishore
AU  - N. Aswinth
AU  - A. RiyazAhamed
PY  - 2026
DA  - 2026/04/24
TI  - Shufflenet-Based Model for Fast and Accurate Brain MRI Detection and Classification
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 473
EP  - 483
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_38
DO  - 10.2991/978-94-6239-654-8_38
ID  - Arulselvi2026
ER  -