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Dergiler / Turkish Journal of Science and Technology / 2026 / Volume: 16 Issue: 1

Classification of Brain Tumor Images using Deep Learning Methods

Harun Bingol(Fırat Üniversitesi)0000-0001-5071-4616
Bilal Alatas(Fırat Üniversitesi)0000-0002-3513-0329
Sayfa
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Yayın Tarihi
2021-03-15
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

With this model, we will try to achieve higher accuracy than any known deep learning method.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Big data refer to all of the information and documents in the form of videos, photographs, text, created by gathering from different sources about a subject.
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Temel Bulgular & Veri:Early diagnosis of the disease greatly increases the patient's chance of survival.
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Sonuç & Katkı:With this model, we will try to achieve higher accuracy than any known deep learning method.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Brain tumor · deep learning · alexnet

Abstract

Big data refer to all of the information and documents in the form of videos, photographs, text, created by gathering from different sources about a subject. Deep learning architectures are often used to reveal hidden information in the big data environment. Brain tumor is a fatal disease that negatively affects human life. Early diagnosis of the disease greatly increases the patient's chance of survival. For this reason, this study was conducted so that doctors could diagnose patients early. In this paper, deep learning architectures Alexnet, Googlenet, and Resnet50 architectures were used to detect brain tumor images. The highest accuracy rate was achieved in the Resnet50 architecture. The accuracy value of 85.71 percent obtained as a result of the experiments will be improved in our future studies. We will try to develop a new method based on convolutional neural networks in the near future. With this model, we will try to achieve higher accuracy than any known deep learning method.

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Bu Makaleye Atıf Yap

Bingol, H., Alatas, B. (2021). Classification of Brain Tumor Images using Deep Learning Methods. Turkish Journal of Science and Technology, Volume: 16 Issue: 1. https://ajindex.com/articles/1557856

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Kaynakça (15)

  1. Wu, X., Zhu, X., Wu, G. Q., Ding, W. (2013). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.
  2. Amin, J., Sharif, M., Yasmin, M., Fernandes, S. L. (2018). Big data analysis for brain tumor detection: Deep convolutional neural networks. Future Generation Computer Systems, 87, 290-297.
  3. URL-1, https://www.acibadem.com.tr/ilgi-alani/beyin-tumorleri/, Last Accessed Date: 27.01.2021
  4. Çinar, A., Yildirim, M. (2020). Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. Medical hypotheses, 139, 109684.
  5. URL-1, https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection, Last Accessed Date: 27.01.2021
  6. Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y. (2017). Automatic brain tumor detection and segmentation using u-net based fully convolutional networks. In annual conference on medical image understanding and analysis (pp. 506-517). Springer, Cham.
  7. Amin, J., Sharif, M., Yasmin, M., Fernandes, S. L. (2017). A distinctive approach in brain tumor detection and classification using MRI. Pattern Recognition Letters.
  8. Wu, M. N., Lin, C. C., Chang, C. C. (2007). Brain tumor detection using color-based k-means clustering segmentation. In Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) (Vol. 2, pp. 245-250). IEEE.
  9. Chandra, G. R., Rao, K. R. H. (2016). Tumor detection in brain using genetic algorithm. Procedia Computer Science, 79, 449-457.
  10. Şeker, A., Diri, B., Balık, H. H. (2017). Derin öğrenme yöntemleri ve uygulamaları hakkında bir inceleme. Gazi Mühendislik Bilimleri Dergisi (GMBD), 3(3), 47-64.
  11. Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.
  12. He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  13. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
  14. Yildirim, M., Cinar, A. (2020). A deep learning based hybrid approach for COVID-19 disease detections. Traitement du Signal, 37(3), 461-468.
  15. Townsend, J. T. (1971). Theoretical analysis of an alphabetic confusion matrix. Perception & Psychophysics, 9(1), 40-50.