MRI Görüntülerini Kullanarak Beyin Tümörü Sınıflandırması için EfficientNetB0 ve EfficientNetV2 Varyantlarının Karşılaştırmalı Analizi
Abstract
Beyin tümörlerinin doğru ve erken teşhisi etkili tedavi planlaması için kritik öneme sahiptir, ancak Manyetik Rezonans Görüntüleme (MRI) taramalarını analiz etmenin geleneksel yöntemleri emek yoğun olup uzmanlar arasında değişkenliğe eğilimlidir. Derin öğrenme, özellikle Evrişimsel Sinir Ağları (CNN'ler), özellik çıkarmayı otomatikleştirerek ve sınıflandırma doğruluğunu artırarak tıbbi görüntülemede dönüştürücü bir araç olarak ortaya çıkmıştır. Bu çalışma, glioma, menenjiyoma ve hipofiz tümörlerini içeren Figshare Beyin Tümörü Veri Setini kullanarak beyin tümörü sınıflandırması için EfficientNetB0 ve üç EfficientNetV2 varyantının (S, M ve L) karşılaştırmalı bir analizini sağlar. Her model doğruluk, kesinlik, geri çağırma, F1 puanı ve ROC-AUC gibi ölçütler kullanılarak değerlendirildi. Sonuçlar, EfficientNetV2-S'nin diğer modellerden daha iyi performans gösterdiğini, %98,20'lik en yüksek doğruluğu elde ettiğini ve tüm sınıflarda dengeli bir performans sağladığını ortaya koymaktadır. EfficientNetV2-M ve EfficientNetV2-L ayrıca hesaplama verimliliğinde küçük ödünlerle güçlü sınıflandırma yetenekleri gösterdi. Bu bulgular, EfficientNetV2 mimarilerinin otomatik ve güvenilir beyin tümörü sınıflandırması için potansiyelini vurgulayarak klinik uygulamalar için önemli avantajlar sunuyor. Gelecekteki çalışmalar, çok modlu görüntüleme verilerini entegre etmeye ve gerçek zamanlı tanılama ortamlarında dağıtım için modelleri optimize etmeye odaklanabilir.
Keywords
Beyin tümörü sınıflandırması Derin öğrenme EfficientNet EfficientNetV2 Tıbbi görüntüleme MRI
References
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A Comparative Analysis of EfficientNetB0 and EfficientNetV2 Variants for Brain Tumor Classification Using MRI Images
Abstract
Accurate and early diagnosis of brain tumors is critical for effective treatment planning, yet traditional methods of analyzing Magnetic Resonance Imaging (MRI) scans are labor-intensive and prone to variability among experts. Deep learning, particularly Convolutional Neural Networks (CNNs), has emerged as a transformative tool in medical imaging by automating feature extraction and enhancing classification accuracy. This study provides a comparative analysis of EfficientNetB0 and three EfficientNetV2 variants (S, M, and L) for brain tumor classification using the Figshare Brain Tumor Dataset, which includes glioma, meningioma, and pituitary tumors. Each model was evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The results reveal that EfficientNetV2-S outperformed other models, achieving the highest accuracy of 98.20% and delivering balanced performance across all classes. EfficientNetV2-M and EfficientNetV2-L also demonstrated strong classification capabilities, with minor trade-offs in computational efficiency. These findings highlight the potential of EfficientNetV2 architectures for automated and reliable brain tumor classification, offering significant advantages for clinical applications. Future work could focus on integrating multi-modal imaging data and optimizing models for deployment in real-time diagnostic settings.
References
- Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Transactions on Medical Imaging, 35(5), 1240–1251.
- Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017). Deep learning for brain MRI segmentation: State of the art and future directions. Journal of Digital Imaging, 30, 449–459.
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
- Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88.
- Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning (pp. 6105–6114).
- Tan, M., Le, Q. V., Shlens, J., Vasudevan, V., & Cheng, Y. (2021). EfficientNetV2: Smaller models and faster training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10096–10106).
- Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 234–241). Springer.
- Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G. Z. (2017). Deep learning for health informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4–17.
- Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221–248.
- Özbay, F. A., & Özbay, E. (2023). Brain tumor detection with mRMR-based multimodal fusion of deep learning from MR images using Grad-CAM. Iran Journal of Computer Science, 6(3), 245–259.
- Yildirim, M., Akkaya, N., & Badem, H. (2023). Detection and classification of glioma, meningioma, pituitary tumor, and normal in brain magnetic resonance imaging using deep learning-based hybrid model. Iran Journal of Computer Science, 6(4), 455–464.
- Islam, M. M., Talukder, M. A., Uddin, M. A., Akhter, A., & Khalid, M. (2024). BrainNet: Precision brain tumor classification with optimized EfficientNet architecture. International Journal of Intelligent Systems, 2024(1), 3583612.
- Gencer, K., & Gencer, G. (2024). Hybrid deep learning approach for brain tumor classification using EfficientNetB0 and novel quantum genetic algorithm. PeerJ Computer Science, 10, e2556.
- Shamshad, N., Sarwr, D., Almogren, A., Saleem, K., Munawar, A., Rehman, A. U., & Bharany, S. (2024). Enhancing brain tumor classification by a comprehensive study on transfer learning techniques and model efficiency using MRI datasets. IEEE Access.
- Cheng, J. (2017). Brain tumor dataset. Figshare.
- Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations.
- Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... & Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
- Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2021). An image is worth 16×16 words: Transformers for image recognition at scale. International Conference on Learning Representations.
Details
| Primary Language | English |
|---|---|
| Subjects | Electrical Engineering (Other) |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | June 30, 2025 |
| Publication Date | June 30, 2025 |
| Submission Date | July 28, 2024 |
| Acceptance Date | April 30, 2025 |
| Published in Issue | Year 2025 Volume: 9 Issue: 1 |
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This work is licensed under CC BY-NC 4.0
