An Intelligent and Lightweight Approach Based on MobilenetV2 Architecture for Identifying Brain Tumors

Duygu Bağcı Daş

doi:10.35377/saucis...1590213

EN TR

An Intelligent and Lightweight Approach Based on MobilenetV2 Architecture for Identifying Brain Tumors

Abstract

Integration of machine learning approaches has the potential to alleviate human error and reduce the time required to diagnose brain tumors by assisting radiologists. The main focus of the existing studies is on developing a model that is as accurate as possible to perform such a task. On the other hand, a model's computational cost and image processing speed are not extensively examined. However, they are significant parameters for the model deployment in real-time. This study aims to close the gap by introducing MobileNetV2-0.5 as a lightweight, fast, and effective approach for identifying brain tumors using real-time Magnetic Resonance Imaging (MRI) images. The results indicated that the proposed approach successfully identified the tumors by 98.78% and detected the non-tumor cases by 99.75%. The computational cost and the processing speed have improved by around 50% compared to the original MobileNetV2 architecture. A similar improvement has also been observed when comparing the proposed approach with the models existing in the literature. Based on the results of the analysis, it is concluded that the proposed MobileNetV2-0.5 has the potential to identify brain tumors in real-time by deploying the model through embedded devices.

Keywords

An Intelligent and Lightweight Approach Based on MobilenetV2 Architecture for Identifying Brain Tumors

Öz

Integration of machine learning approaches has the potential to alleviate human error and reduce the time required to diagnose brain tumors by assisting radiologists. The main focus of the existing studies is on developing a model that is as accurate as possible to perform such a task. On the other hand, a model's computational cost and image processing speed are not extensively examined. However, they are significant parameters for the model deployment in real-time. This study aims to close the gap by introducing MobileNetV2-0.5 as a lightweight, fast, and effective approach for identifying brain tumors using real-time Magnetic Resonance Imaging (MRI) images. The results indicated that the proposed approach successfully identified the tumors by 98.78% and detected the non-tumor cases by 99.75%. The computational cost and the processing speed have improved by around 50% compared to the original MobileNetV2 architecture. A similar improvement has also been observed when comparing the proposed approach with the models existing in the literature. Based on the results of the analysis, it is concluded that the proposed MobileNetV2-0.5 has the potential to identify brain tumors in real-time by deploying the model through embedded devices.

Anahtar Kelimeler

References

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Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

Duygu Bağcı Daş ^*
0000-0003-4519-3531
Türkiye

Early Pub Date

September 24, 2025

Publication Date

September 30, 2025

Submission Date

November 23, 2024

Acceptance Date

June 16, 2025

Published in Issue

Year 2025 Volume: 8 Number: 3

DOI

https://doi.org/10.35377/saucis...1590213

IZ

https://izlik.org/JA87NF67ZZ

Cite

RIS / Bibtex

APA

Bağcı Daş, D. (2025). An Intelligent and Lightweight Approach Based on MobilenetV2 Architecture for Identifying Brain Tumors. Sakarya University Journal of Computer and Information Sciences, 8(3), 392-399. https://doi.org/10.35377/saucis...1590213

AMA

1.Bağcı Daş D. An Intelligent and Lightweight Approach Based on MobilenetV2 Architecture for Identifying Brain Tumors. SAUCIS. 2025;8(3):392-399. doi:10.35377/saucis.1590213

Chicago

Bağcı Daş, Duygu. 2025. “An Intelligent and Lightweight Approach Based on MobilenetV2 Architecture for Identifying Brain Tumors”. Sakarya University Journal of Computer and Information Sciences 8 (3): 392-99. https://doi.org/10.35377/saucis. 1590213.

EndNote

Bağcı Daş D (September 1, 2025) An Intelligent and Lightweight Approach Based on MobilenetV2 Architecture for Identifying Brain Tumors. Sakarya University Journal of Computer and Information Sciences 8 3 392–399.

IEEE

[1]D. Bağcı Daş, “An Intelligent and Lightweight Approach Based on MobilenetV2 Architecture for Identifying Brain Tumors”, SAUCIS, vol. 8, no. 3, pp. 392–399, Sept. 2025, doi: 10.35377/saucis...1590213.

ISNAD

Bağcı Daş, Duygu. “An Intelligent and Lightweight Approach Based on MobilenetV2 Architecture for Identifying Brain Tumors”. Sakarya University Journal of Computer and Information Sciences 8/3 (September 1, 2025): 392-399. https://doi.org/10.35377/saucis. 1590213.

JAMA

1.Bağcı Daş D. An Intelligent and Lightweight Approach Based on MobilenetV2 Architecture for Identifying Brain Tumors. SAUCIS. 2025;8:392–399.

MLA

Bağcı Daş, Duygu. “An Intelligent and Lightweight Approach Based on MobilenetV2 Architecture for Identifying Brain Tumors”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 3, Sept. 2025, pp. 392-9, doi:10.35377/saucis. 1590213.

Vancouver

1.Duygu Bağcı Daş. An Intelligent and Lightweight Approach Based on MobilenetV2 Architecture for Identifying Brain Tumors. SAUCIS. 2025 Sep. 1;8(3):392-9. doi:10.35377/saucis. 1590213