Araştırma Makalesi

Brain Tumor Detection using Deep CNNs and Ensemble Algorithms over MRI Images

Cilt: 9 Sayı: Issue: 2 25 Aralık 2024
PDF İndir
EN TR

Brain Tumor Detection using Deep CNNs and Ensemble Algorithms over MRI Images

Öz

Brain tumors are one of the most common causes of human death. Early and accurate diagnosis of brain tumors is very important for effective treatment. Different learning techniques have been used in the field of health to diagnose diseases early and reduce the intensity of experts, as well as to minimize errors that may be made in diagnosis. In recent years, successful results have begun to be obtained in image processing studies in brain research, with the development of machine learning and deep learning models. In this study, pretrained deep convolution neural network methods are preferred to feature extraction from MRI images, and ensemble learning is performed to detect the tumor from extracted features. Analysis results show a 100% accuracy score, using the ensemble-based classifier with the pretrained deep networks to detect brain tumors.

Anahtar Kelimeler

Kaynakça

  1. Bauer, E., and Kohavi, R., 1998. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning, 1-38.
  2. Bishop, C., 2010. Neural Networks for Pattern Recognition, Oxford University Press.
  3. Bishop, C.M., 1995. Neural Network for Pattern Recognition, Microsoft Research Cambridge.
  4. Bishop, C.M., 2006. Pattern Recognition and Machine Learning, Springer.
  5. Breiman, L., 1996. Bagging Predictors, Vol. 24, Kluwer Academic Publishers.
  6. Dong, Y., Zhang, H., Wang, C., and Wang, Y., 2019. Fine-Grained Ship Classification based on Deep Residual Learning for High-Resolution SAR Images, Remote Sens. Lett., 10 (11), 1095-1104.
  7. Efron, Bradley., & Tibshirani, Robert. (1994). An introduction to the bootstrap. Chapman & Hall.
  8. Gao, H., Zhuang, L., Laurens, van der M., and Kilian Q.W., 2018. Densely Connected Convolutional Networks, Computer Science > Computer Vision and Pattern Recognition, arXiv:1608.06993v5 , https://doi.org/10.48550/arXiv.1608.06993

Ayrıntılar

Birincil Dil

İngilizce

Konular

Derin Öğrenme, Makine Öğrenme (Diğer)

Bölüm

Araştırma Makalesi

Erken Görünüm Tarihi

24 Aralık 2024

Yayımlanma Tarihi

25 Aralık 2024

Gönderilme Tarihi

20 Mart 2024

Kabul Tarihi

6 Ağustos 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 9 Sayı: Issue: 2

Kaynak Göster

APA
Özer, E. (2024). Brain Tumor Detection using Deep CNNs and Ensemble Algorithms over MRI Images. Computer Science, 9(Issue: 2), 142-150. https://doi.org/10.53070/bbd.1455902
AMA
1.Özer E. Brain Tumor Detection using Deep CNNs and Ensemble Algorithms over MRI Images. JCS. 2024;9(Issue: 2):142-150. doi:10.53070/bbd.1455902
Chicago
Özer, Ezgi. 2024. “Brain Tumor Detection using Deep CNNs and Ensemble Algorithms over MRI Images”. Computer Science 9 (Issue: 2): 142-50. https://doi.org/10.53070/bbd.1455902.
EndNote
Özer E (01 Aralık 2024) Brain Tumor Detection using Deep CNNs and Ensemble Algorithms over MRI Images. Computer Science 9 Issue: 2 142–150.
IEEE
[1]E. Özer, “Brain Tumor Detection using Deep CNNs and Ensemble Algorithms over MRI Images”, JCS, c. 9, sy Issue: 2, ss. 142–150, Ara. 2024, doi: 10.53070/bbd.1455902.
ISNAD
Özer, Ezgi. “Brain Tumor Detection using Deep CNNs and Ensemble Algorithms over MRI Images”. Computer Science 9/Issue: 2 (01 Aralık 2024): 142-150. https://doi.org/10.53070/bbd.1455902.
JAMA
1.Özer E. Brain Tumor Detection using Deep CNNs and Ensemble Algorithms over MRI Images. JCS. 2024;9:142–150.
MLA
Özer, Ezgi. “Brain Tumor Detection using Deep CNNs and Ensemble Algorithms over MRI Images”. Computer Science, c. 9, sy Issue: 2, Aralık 2024, ss. 142-50, doi:10.53070/bbd.1455902.
Vancouver
1.Ezgi Özer. Brain Tumor Detection using Deep CNNs and Ensemble Algorithms over MRI Images. JCS. 01 Aralık 2024;9(Issue: 2):142-50. doi:10.53070/bbd.1455902

Cited By

The Creative Commons Attribution 4.0 International License 88x31.png  is applied to all research papers published by JCS and

a Digital Object Identifier (DOI)     Logo_TM.png  is assigned for each published paper.