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Journals / Bilişim Teknolojileri Dergisi / 2026 / Cilt: 19 Sayı: 3

Comparative Analysis of Modern CNN, Transformer, and Lightweight Architectures for Multi-Class Brain Tumor Classification from MRI Data

MRI Verilerinden Çok Sınıflı Beyin Tümörü Sınıflandırması için Modern CNN, Transformer ve Hafif Mimarilerin Karşılaştırmalı Analizi

Sümeyye Demir(Karadeniz Teknik Üniversitesi)0009-0004-7630-1472
Muhammet Akgül(Beykent Üniversitesi)0009-0009-4633-4579
Pages
299–313
Publication Date
2026-07-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experimental findings revealed that under the provided conditions, the Xception architecture exhibited the highest accuracy, offering superior stability, particularly in distinguishing healthy tissues.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Early diagnosis of brain tumors is vital for clinical treatment success.
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Key Findings & Data:This study comparatively analyzes the performance of Xception, MobileNetV3-Large, ConvNeXt-Tiny, and Vision Transformer models in classifying magnetic resonance imaging data in four different categories (glioma, meningioma, pituitary tumor, and no tumor).
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Conclusion & Impact:Experimental findings revealed that under the provided conditions, the Xception architecture exhibited the highest accuracy, offering superior stability, particularly in distinguishing healthy tissues.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:beyin tümörü tespiti · manyetik rezonans görüntüleme · evrişimsel sinir ağı

Abstract

Early diagnosis of brain tumors is vital for clinical treatment success. This study comparatively analyzes the performance of Xception, MobileNetV3-Large, ConvNeXt-Tiny, and Vision Transformer models in classifying magnetic resonance imaging data in four different categories (glioma, meningioma, pituitary tumor, and no tumor). Experimental findings revealed that under the provided conditions, the Xception architecture exhibited the highest accuracy, offering superior stability, particularly in distinguishing healthy tissues. While the MobileNetV3-Large and ConvNeXt-Tiny models yielded competitive results, the ViT model showed limited success compared to the other models. In terms of computational efficiency, MobileNetV3-Large was the fastest, while ConvNeXt-Tiny was the most expensive model. The results demonstrate that powerful convolutional neural networks offer a strategic advantage in medical decision support systems requiring high accuracy.

Özet

Beyin tümörlerinin erken teşhisi, klinik tedavi başarısı için hayati önem taşımaktadır. Bu çalışma 4 farklı kategoride (glioma, meningioma, hipofiz tümörü ve tümör yok) manyetik rezonans görüntüleme verileri üzerinden sınıflandırılmasında Xception, MobileNetV3-Large, ConvNeXt-Tiny ve Vision Transformer modellerinin performansını karşılaştırmalı olarak analiz etmektedir. Deneysel bulgular, sağlanan koşullar altında Xception mimarisinin en yüksek doğruluğu sergileyerek özellikle sağlıklı dokuların ayırt edilmesinde üstün bir kararlılık sunduğunu ortaya koymuştur. MobileNetV3-Large ve ConvNeXt-Tiny modelleri rekabetçi sonuçlar verirken, ViT modelinin diğer modellere kıyasla başarısının sınırlı kaldığı gözlemlenmiştir. Hesaplama verimliliği açısından MobileNetV3-Large en hızlı, ConvNeXt-Tiny ise maliyeti en yüksek model olmuştur. Sonuçlar, yüksek hassasiyet gerektiren tıbbi karar destek sistemlerinde güçlü evrişimsel sinir ağlarının stratejik bir avantaj sunduğunu kanıtlamaktadır.

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Cite This Article

Demir, S., Akgül, M. (2026). Comparative Analysis of Modern CNN, Transformer, and Lightweight Architectures for Multi-Class Brain Tumor Classification from MRI Data. Bilişim Teknolojileri Dergisi, Cilt: 19 Sayı: 3, 299–313. https://doi.org/10.17671/gazibtd.1852936

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References (30)

  1. 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,” Front. Bioeng. Biotechnol., vol. 12, p. 1392807, July 2024. https://doi.org/10.3389/fbioe.2024.1392807
  2. J. Stiles and T. L. Jernigan, “The Basics of Brain Development,” Neuropsychol. Rev., vol. 20, no. 4, pp. 327–348, Dec. 2010. https://doi.org/10.1007/s11065-010-9148-4
  3. S. M. Kulkarni and G. Sundari, “A Framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 8, 2020. https://doi.org/10.14569/IJACSA.2020.0110848.
  4. M. Havaei et al., “Brain tumor segmentation with Deep Neural Networks,” Med. Image Anal., vol. 35, pp. 18–31, Jan. 2017. https://doi.org/10.1016/j.media.2016.05.004.
  5. M. Talo, U. B. Baloglu, Ö. Yıldırım, and U. Rajendra Acharya, “Application of deep transfer learning for automated brain abnormality classification using MR images,” Cogn. Syst. Res., vol. 54, pp. 176–188, May 2019. https://doi.org/10.1016/j.cogsys.2018.12.007.
  6. S. Deepak and P. M. Ameer, “Brain tumor classification using deep CNN features via transfer learning,” Comput. Biol. Med., vol. 111, p. 103345, Aug. 2019. https://doi.org/10.1016/j.compbiomed.2019.103345.
  7. A. Amarnath, A. Al Bataineh, and J. A. Hansen, “Transfer-Learning Approach for Enhanced Brain Tumor Classification in MRI Imaging,” BioMedInformatics, vol. 4, no. 3, pp. 1745–1756, July 2024. https://doi.org/10.3390/biomedinformatics4030095.
  8. Q. T. Ostrom et al., “CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2015–2019,” Neuro-Oncol., vol. 24, no. Supplement_5, pp. v1–v95, Oct. 2022. https://doi.org/10.1093/neuonc/noac202.
  9. R. Disci, F. Gurcan, and A. Soylu, “Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models,” Cancers, vol. 17, no. 1, p. 121, Jan. 2025. https://doi.org/10.3390/cancers17010121.
  10. D. Lamrani, B. Cherradi, O. E. Gannour, M. A. Bouqentar, and L. Bahatti, “Brain Tumor Detection using MRI Images and Convolutional Neural Network,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 7, 2022. https://doi.org/10.14569/IJACSA.2022.0130755.
  11. M. I. Mahmud, M. Mamun, and A. Abdelgawad, “A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks,” Algorithms, vol. 16, no. 4, p. 176, Mar. 2023. https://doi.org/10.3390/a16040176.
  12. S. Mandal et al., “Artificial Intelligence and Deep Learning in Revolutionizing Brain Tumor Diagnosis and Treatment: A Narrative Review,” Cureus, Aug. 2024. https://doi.org/10.7759/cureus.66157.
  13. D. R. Nayak, N. Padhy, P. K. Mallick, M. Zymbler, and S. Kumar, “Brain Tumor Classification Using Dense Efficient-Net,” Axioms, vol. 11, no. 1, p. 34, Jan. 2022. https://doi.org/10.3390/axioms11010034.
  14. Z. N. K. Swati et al., “Brain tumor classification for MR images using transfer learning and fine-tuning,” Comput. Med. Imaging Graph., vol. 75, pp. 34–46, July 2019. https://doi.org/10.1016/j.compmedimag.2019.05.001
  15. M. Siar and M. Teshnehlab, “Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm,” in 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran: IEEE, Oct. 2019, pp. 363–368. https://doi.org/10.1109/ICCKE48569.2019.8964846.
  16. A. S. Musallam, A. S. Sherif, and M. K. Hussein, “A New Convolutional Neural Network Architecture for Automatic Detection of Brain Tumors in Magnetic Resonance Imaging Images,” IEEE Access, vol. 10, pp. 2775–2782, 2022. https://doi.org/10.1109/ACCESS.2022.3140289.
  17. Internet: Msoud Nickparvar, “Brain Tumor MRI Dataset.” https://doi.org/10.34740/kaggle/dsv/2645886. (Latest access:31.12.2025)
  18. D. N. Louis et al., “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary,” Acta Neuropathol. (Berl.), vol. 131, no. 6, pp. 803–820, June 2016. https://doi.org/10.1007/s00401-016-1545-1.
  19. A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” June 03, 2021, arXiv: arXiv:2010.11929. https://doi.org/10.48550/arXiv.2010.11929.
  20. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, May 2017. https://doi.org/10.1145/3065386.
  21. R. C. Gonzalez, R. E. Woods, and B. R. Masters, “Digital Image Processing, Third Edition,” J. Biomed. Opt., vol. 14, no. 2, p. 029901, 2009. https://doi.org/10.1117/1.3115362.
  22. C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 1, p. 60, Dec. 2019. https://doi.org/10.1186/s40537-019-0197-0.
  23. Y. Bengio, “Learning Deep Architectures for AI”. https://doi.org/10.1561/2200000006.
  24. G. E. Hinton, S. Osindero, and Y.-W. Teh, “A Fast Learning Algorithm for Deep Belief Nets,” Neural Comput., vol. 18, no. 7, pp. 1527–1554, July 2006. https://doi.org/10.1162/neco.2006.18.7.1527.
  25. A. Howard et al., “Searching for MobileNetV3,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South): IEEE, Oct. 2019, pp. 1314–1324. https://doi.org/10.1109/ICCV.2019.00140.
  26. F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI: IEEE, July 2017, pp. 1800–1807. https://doi.org/10.1109/CVPR.2017.195.
  27. S. Woo et al., “ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada: IEEE, June 2023, pp. 16133–16142. https://doi.org/10.1109/CVPR52729.2023.01548.
  28. S. M. Anwar, M. Majid, A. Qayyum, M. Awais, M. Alnowami, and M. K. Khan, “Medical Image Analysis using Convolutional Neural Networks: A Review,” J. Med. Syst., vol. 42, no. 11, p. 226, Nov. 2018. https://doi.org/10.1007/s10916-018-1088-1.
  29. Z. Rasheed, Y. K. Ma, I. Ullah, Y. Y. Ghadi, M. Z. Khan, M. A. Khan, A. Abdusalomov, F. Alqahtani, A. M. Shehata, "Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques", Brain Sciences, 13(9), 1320, 2023.
  30. H. A. Khan, W. Jue, M. Mushtaq, M. U. Mushtaq, "Brain tumor classification in MRI image using convolutional neural network", Mathematical Biosciences and Engineering, 17(5), 6203-6216, 2020.