Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network

Abstract Tumor in brain is an anthology of anomalous cells. It leads to increase in death rate among humans. Therefore, in this manuscript, a fusion process is proposed to combine structural and texture information of four MRI sequences (T1C, T1, Flair and T2) for the detection of brain tumor. A discrete wavelet transform (DWT) along with Daubechies wavelet kernel is utilized for fusion process which provides a more informative tumor region as compared to an individual single sequence of MRI. After the fusion process, a partial differential diffusion filter (PDDF) is applied to remove noise. A global thresholding method is used for segmenting tumor region which is then fed to proposed convolutional neural network (CNN) model for finally differentiating tumor and non-tumor regions. Five publicly available datasets i.e., BRATS 2012, BRATS 2013, BRATS 2015, BRATS 2013 Leader board and BRATS 2018 are used for proposed method evaluation. The results show that fused images provide better results as compared to individual sequences on benchmark datasets.

• Publication year

  2020

• Venue

  Pattern Recognition Letters

• Publication date

  2020-01-01

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1016/j.patrec.2019.11.016
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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