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

•New method proposed for brain tumor classification based on fused MRI sequences.•T1, T1C, T2 and Flair MRI sequences are fused using discrete wavelet transform.•A global thresholding method is utilized to segment tumor region.•The segmented images are passed to 23 layers CNN model for classification.•Presented approach evaluation on BRATS datasets to authenticate its performance. 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.