Segmentation and Classification of Brain Tumour Images

Authors

  • Shreeja. M  Computer Science and Engineering, Srinivas Institute of Technology Valachil, Mangalure, Karnaatka, India
  • Mrs. Padmanayana  Computer Science and Engineering, Srinivas Institute of Technology Valachil, Mangalure, Karnaatka, India

DOI:

https://doi.org/10.32628/CSEIT217412

Keywords:

A tumor in the brain is an uncontrolled and abnormal cell proliferation in the brain and are categorized into four levels. Each tumor grade has its own set of contrast variations, that are captured using the MRI technology. Different computational models are used to accurately segment these cancers and to classify it. In this research, we present a method for segmenting brain tumors that uses a model based on deep learning called U-Net, and also classification of the segmented images is done using Random-Forest classifier. The proposed method was tested on the Brain Tumor Image Segmentation (BRATS) 2015 dataset and showed to be effective. With the best overall accuracy of 77 percent, the proposed network structure achieves a remarkable performance.

Abstract

Brain tumor, U-Net, Random Forest

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-08-30

Issue

Volume 7, Issue 4, July-August-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Shreeja. M, Mrs. Padmanayana, " Segmentation and Classification of Brain Tumour Images" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 4, pp.297-302, July-August-2021. Available at doi : https://doi.org/10.32628/CSEIT217412