Research Article

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

Volume: 9 Number: Issue: 2 December 25, 2024
EN TR

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

Abstract

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.

Keywords

References

  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

Details

Primary Language

English

Subjects

Deep Learning, Machine Learning (Other)

Journal Section

Research Article

Early Pub Date

December 24, 2024

Publication Date

December 25, 2024

Submission Date

March 20, 2024

Acceptance Date

August 6, 2024

Published in Issue

Year 2024 Volume: 9 Number: Issue: 2

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 (December 1, 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, vol. 9, no. Issue: 2, pp. 142–150, Dec. 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 (December 1, 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, vol. 9, no. Issue: 2, Dec. 2024, pp. 142-50, doi:10.53070/bbd.1455902.
Vancouver
1.Ezgi Özer. Brain Tumor Detection using Deep CNNs and Ensemble Algorithms over MRI Images. JCS. 2024 Dec. 1;9(Issue: 2):142-50. doi:10.53070/bbd.1455902

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