Research Article
BibTex RIS Cite

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

Year 2025, Volume: 8 Issue: 3, 392 - 399, 30.09.2025

Duygu Bağcı Daş

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

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

Brain tumor detection MobileNetV2 Deep Learning Machine Learning

References

  • M. Woźniak, J. Siłka, and M. Wieczorek, “Deep neural network correlation learning mechanism for CT brain tumor detection,” Neural Computing and Applications, vol. 35, no. 20, pp. 14611–14626, 2021. doi: 10.1007/s00521-021-05841-x.
  • K. Demir, B. Arı, and F. Demir, “Detection of brain tumor with a pre-trained deep learning model based on feature selection using MR images,” Firat University Journal of Experimental and Computational Engineering, vol. 2, no. 1, pp. 23–31, 2023. doi: 10.5505/fujece.2023.36844.
  • N. Rasool and J. I. Bhat, “Brain tumour detection using machine and deep learning: a systematic review,” Multimedia Tools and Applications, 2024. doi: 10.1007/s11042-024-19333-2.
  • Z. U. Abidin, R. A. Naqvi, A. Haider, H. S. Kim, D. Jeong, and S. W. Lee, “Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey,” Frontiers in Bioengineering and Biotechnology, vol. 12, 2024. doi: 10.3389/fbioe.2024.1392807.
  • S. Bouhafra and H. E. Bahi, “Deep Learning Approaches for Brain Tumor Detection and Classification using MRI Images (2020 to 2024): A Systematic review,” Journal of Imaging Informatics in Medicine, 2024. doi: 10.1007/s10278-024-01283-8.
  • R. Ibrahim, R. Ghnemat, and Q. A. Al-Haija, “Improving Alzheimer’s Disease and Brain Tumor Detection Using Deep Learning with Particle Swarm Optimization,” AI, vol. 4, no. 3, pp. 551–573, 2023. doi: 10.3390/ai4030030.
  • M. Siar and M. Teshnehlab, “Brain tumor detection using deep neural network and machine learning algorithm”, 9th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 363–368, 2019. doi: 10.1109/iccke48569.2019.8964846.
  • R. Asad, S. U. Rehman, A. Imran, J. Li, A. Almuhaimeed, and A. Alzahrani, “Computer-Aided Early Melanoma Brain-Tumor Detection using Deep-Learning approach,” Biomedicines, vol. 11, no. 1, p. 184, 2023. doi: 10.3390/biomedicines11010184.
  • P. Kanchanamala, R. KG, and M. B. J. Ananth, “Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI,” Biomedical Signal Processing and Control, vol. 84, p. 104955, 2023. doi: 10.1016/j.bspc.2023.104955.
  • S. Patil and D. Kirange, “Ensemble of deep learning models for brain tumor detection,” Procedia Computer Science, vol. 218, pp. 2468–2479, 2023. doi: 10.1016/j.procs.2023.01.222.
  • S. Kumar, A. Kumar, and A. Jaiswal, “A Low Complexity MobileNetV2 based CNN Model for Brain Tumor Detection in MRI Images”, IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), pp. 1–7, 2024. doi: 10.1109/iaict62357.2024.10617450.
  • J.N. Benedict, C. Balasubramanian, S. P. Senthil, and P. Kumar , “Revolutionizing Brain Tumor Classification: A Hybrid Model Incorporating CNN-Based Multiscale Feature Extraction and MobileNet V2”, 2024 Second International Conference on Advances in Information Technology (ICAIT), pp. 1–6, 2024. doi: 10.1109/icait61638.2024.10690820.
  • R. R. Sharmily, B. Karthik, and T. Vijayan, “Brain Tumour Detection and Classification using Deep Learning And Transfer Learning Techniques”, 2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS)”, pp. 01–05, 2023. doi: 10.1109/iccebs58601.2023.10449015.
  • T. Sadad, A. Rehman, A. Munir, T. Saba, U. Tariq, N. Ayesha, and R. Abbasi, “Brain tumor detection and multi‐classification using advanced deep learning techniques,” Microscopy Research and Technique, vol. 84, no. 6, pp. 1296–1308, 2021. doi: 10.1002/jemt.23688.
  • A. B. Abdusalomov, M. Mukhiddinov, and T. K. Whangbo, “Brain tumor detection based on deep learning approaches and magnetic resonance imaging,” Cancers, vol. 15, no. 16, p. 4172, 2023. doi: 10.3390/cancers15164172.
  • A. Ari and D. Hanbay, “Deep learning based brain tumor classification and detection system,” Turkısh Journal of Electrıcal Engıneerıng & Computer Scıences, vol. 26, no. 5, pp. 2275–2286, 2018. doi: 10.3906/elk-1801-8.
  • S. Saeedi, S. Rezayi, H. Keshavarz, and S. R. N. Kalhori, “MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques,” BMC Medical Informatics and Decision Making, vol. 23, no. 1, Jan. 2023, doi: 10.1186/s12911-023-02114-6.
  • A. G. Balamurugan, S. Srinivasan, D. Preethi, P. Monica, S. K. Mathivanan, and M. A. Shah, “Robust brain tumor classification by fusion of deep learning and channel-wise attention mode approach,” BMC Medical Imaging, vol. 24, no. 1, Jun. 2024, doi: 10.1186/s12880-024-01323-3.
  • M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks”, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510–4520, 2018. doi: 10.1109/cvpr.2018.00474.
  • Chaki, “Brain tumor MRI dataset,” IEEE DataPort, 2025. doi: 10.21227/1jny-g144
  • M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 6105-6114, 2019. doi: 10.48550/arxiv.1905.11946. Available: https://arxiv.org/abs/1905.11946
  • A. Howard et al., “Searching for MobileNetV3,” 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1314–1324, 2019. doi: 10.1109/iccv.2019.00140. https://doi.org/10.1109/iccv.2019.00140
  • Y. Hou, Z. Ma, C. Liu, Z. Wang, and C. C. Loy, “Network pruning via resource reallocation,” Pattern Recognition, vol. 145, p. 109886, 2023. doi: 10.1016/j.patcog.2023.109886.
  • N. Tüzün and D. Özdemi̇r, “Classification of brain tumors with deep learning models,” Journal of Scientific Reports-A, no. 054, pp. 296–306, 2023. doi: 10.59313/jsr-a.1293119.
  • A. R. Khan, S. Khan, M. Harouni, R. Abbasi, S. Iqbal, and Z. Mehmood, “Brain tumor segmentation using K‐means clustering and deep learning with synthetic data augmentation for classification,” Microscopy Research and Technique, vol. 84, no. 7, pp. 1389–1399, 2021. doi: 10.1002/jemt.23694.
  • H. Yahyaoui, F. Ghazouani, and I. R. Farah, “Deep learning guided by an ontology for medical images classification using a multimodal fusion,” 2021 International Congress of Advanced Technology and Engineering (ICOTEN), pp. 1–6, 2021. doi: 10.1109/icoten52080.2021.9493469.
  • Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” Journal of Big Data, vol. 6, no. 1, 2019. doi: 10.1186/s40537-019-0197-0.
  • C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision”, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826, 2016. doi: 10.1109/cvpr.2016.308.

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

Year 2025, Volume: 8 Issue: 3, 392 - 399, 30.09.2025

Duygu Bağcı Daş

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

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

Brain tumor detection MobileNetV2 Deep Learning Machine Learning

References

  • M. Woźniak, J. Siłka, and M. Wieczorek, “Deep neural network correlation learning mechanism for CT brain tumor detection,” Neural Computing and Applications, vol. 35, no. 20, pp. 14611–14626, 2021. doi: 10.1007/s00521-021-05841-x.
  • K. Demir, B. Arı, and F. Demir, “Detection of brain tumor with a pre-trained deep learning model based on feature selection using MR images,” Firat University Journal of Experimental and Computational Engineering, vol. 2, no. 1, pp. 23–31, 2023. doi: 10.5505/fujece.2023.36844.
  • N. Rasool and J. I. Bhat, “Brain tumour detection using machine and deep learning: a systematic review,” Multimedia Tools and Applications, 2024. doi: 10.1007/s11042-024-19333-2.
  • Z. U. Abidin, R. A. Naqvi, A. Haider, H. S. Kim, D. Jeong, and S. W. Lee, “Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey,” Frontiers in Bioengineering and Biotechnology, vol. 12, 2024. doi: 10.3389/fbioe.2024.1392807.
  • S. Bouhafra and H. E. Bahi, “Deep Learning Approaches for Brain Tumor Detection and Classification using MRI Images (2020 to 2024): A Systematic review,” Journal of Imaging Informatics in Medicine, 2024. doi: 10.1007/s10278-024-01283-8.
  • R. Ibrahim, R. Ghnemat, and Q. A. Al-Haija, “Improving Alzheimer’s Disease and Brain Tumor Detection Using Deep Learning with Particle Swarm Optimization,” AI, vol. 4, no. 3, pp. 551–573, 2023. doi: 10.3390/ai4030030.
  • M. Siar and M. Teshnehlab, “Brain tumor detection using deep neural network and machine learning algorithm”, 9th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 363–368, 2019. doi: 10.1109/iccke48569.2019.8964846.
  • R. Asad, S. U. Rehman, A. Imran, J. Li, A. Almuhaimeed, and A. Alzahrani, “Computer-Aided Early Melanoma Brain-Tumor Detection using Deep-Learning approach,” Biomedicines, vol. 11, no. 1, p. 184, 2023. doi: 10.3390/biomedicines11010184.
  • P. Kanchanamala, R. KG, and M. B. J. Ananth, “Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI,” Biomedical Signal Processing and Control, vol. 84, p. 104955, 2023. doi: 10.1016/j.bspc.2023.104955.
  • S. Patil and D. Kirange, “Ensemble of deep learning models for brain tumor detection,” Procedia Computer Science, vol. 218, pp. 2468–2479, 2023. doi: 10.1016/j.procs.2023.01.222.
  • S. Kumar, A. Kumar, and A. Jaiswal, “A Low Complexity MobileNetV2 based CNN Model for Brain Tumor Detection in MRI Images”, IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), pp. 1–7, 2024. doi: 10.1109/iaict62357.2024.10617450.
  • J.N. Benedict, C. Balasubramanian, S. P. Senthil, and P. Kumar , “Revolutionizing Brain Tumor Classification: A Hybrid Model Incorporating CNN-Based Multiscale Feature Extraction and MobileNet V2”, 2024 Second International Conference on Advances in Information Technology (ICAIT), pp. 1–6, 2024. doi: 10.1109/icait61638.2024.10690820.
  • R. R. Sharmily, B. Karthik, and T. Vijayan, “Brain Tumour Detection and Classification using Deep Learning And Transfer Learning Techniques”, 2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS)”, pp. 01–05, 2023. doi: 10.1109/iccebs58601.2023.10449015.
  • T. Sadad, A. Rehman, A. Munir, T. Saba, U. Tariq, N. Ayesha, and R. Abbasi, “Brain tumor detection and multi‐classification using advanced deep learning techniques,” Microscopy Research and Technique, vol. 84, no. 6, pp. 1296–1308, 2021. doi: 10.1002/jemt.23688.
  • A. B. Abdusalomov, M. Mukhiddinov, and T. K. Whangbo, “Brain tumor detection based on deep learning approaches and magnetic resonance imaging,” Cancers, vol. 15, no. 16, p. 4172, 2023. doi: 10.3390/cancers15164172.
  • A. Ari and D. Hanbay, “Deep learning based brain tumor classification and detection system,” Turkısh Journal of Electrıcal Engıneerıng & Computer Scıences, vol. 26, no. 5, pp. 2275–2286, 2018. doi: 10.3906/elk-1801-8.
  • S. Saeedi, S. Rezayi, H. Keshavarz, and S. R. N. Kalhori, “MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques,” BMC Medical Informatics and Decision Making, vol. 23, no. 1, Jan. 2023, doi: 10.1186/s12911-023-02114-6.
  • A. G. Balamurugan, S. Srinivasan, D. Preethi, P. Monica, S. K. Mathivanan, and M. A. Shah, “Robust brain tumor classification by fusion of deep learning and channel-wise attention mode approach,” BMC Medical Imaging, vol. 24, no. 1, Jun. 2024, doi: 10.1186/s12880-024-01323-3.
  • M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks”, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510–4520, 2018. doi: 10.1109/cvpr.2018.00474.
  • Chaki, “Brain tumor MRI dataset,” IEEE DataPort, 2025. doi: 10.21227/1jny-g144
  • M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 6105-6114, 2019. doi: 10.48550/arxiv.1905.11946. Available: https://arxiv.org/abs/1905.11946
  • A. Howard et al., “Searching for MobileNetV3,” 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1314–1324, 2019. doi: 10.1109/iccv.2019.00140. https://doi.org/10.1109/iccv.2019.00140
  • Y. Hou, Z. Ma, C. Liu, Z. Wang, and C. C. Loy, “Network pruning via resource reallocation,” Pattern Recognition, vol. 145, p. 109886, 2023. doi: 10.1016/j.patcog.2023.109886.
  • N. Tüzün and D. Özdemi̇r, “Classification of brain tumors with deep learning models,” Journal of Scientific Reports-A, no. 054, pp. 296–306, 2023. doi: 10.59313/jsr-a.1293119.
  • A. R. Khan, S. Khan, M. Harouni, R. Abbasi, S. Iqbal, and Z. Mehmood, “Brain tumor segmentation using K‐means clustering and deep learning with synthetic data augmentation for classification,” Microscopy Research and Technique, vol. 84, no. 7, pp. 1389–1399, 2021. doi: 10.1002/jemt.23694.
  • H. Yahyaoui, F. Ghazouani, and I. R. Farah, “Deep learning guided by an ontology for medical images classification using a multimodal fusion,” 2021 International Congress of Advanced Technology and Engineering (ICOTEN), pp. 1–6, 2021. doi: 10.1109/icoten52080.2021.9493469.
  • Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” Journal of Big Data, vol. 6, no. 1, 2019. doi: 10.1186/s40537-019-0197-0.
  • C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision”, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826, 2016. doi: 10.1109/cvpr.2016.308.
There are 28 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Article
Authors

Duygu Bağcı Daş 0000-0003-4519-3531

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 Issue: 3

Cite

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 Bağcı Daş D. An Intelligent and Lightweight Approach Based on MobilenetV2 Architecture for Identifying Brain Tumors. SAUCIS. September 2025;8(3):392-399. doi:10.35377/saucis.1590213
Chicago 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, no. 3 (September 2025): 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 D. Bağcı Daş, “An Intelligent and Lightweight Approach Based on MobilenetV2 Architecture for Identifying Brain Tumors”, SAUCIS, vol. 8, no. 3, pp. 392–399, 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 (September2025), 392-399. https://doi.org/10.35377/saucis. 1590213.
JAMA 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, 2025, pp. 392-9, doi:10.35377/saucis. 1590213.
Vancouver Bağcı Daş D. An Intelligent and Lightweight Approach Based on MobilenetV2 Architecture for Identifying Brain Tumors. SAUCIS. 2025;8(3):392-9.
 Download Cover Image


INDEXING & ABSTRACTING & ARCHIVING


 31045 31044   ResimLink - Resim Yükle  31047 

31043 28939 28938 34240


29070    The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License