Background: Accurate detection of brain tumor and its severity is a challenging task in
the medical field. So there is a need for developing brain tumor detecting algorithms and it is an
emerging one for diagnosis, planning the treatment and outcome evaluation.
Method: Brain tumor segmentation method using deep learning classification and multi-modal
composition has been developed using the deep convolutional neural networks. The different modalities
of MRI such as T1, flair, T1C and T2 are given as input for the proposed method. The MR
images from the different modalities are used in proportion to the information contents in the particular
modality. The weights for the different modalities are calculated blockwise and the standard
deviation of the block is taken as a proxy for the information content of the block. Then the
convolution is performed between the input image of the T1, flair, T1C and T2 MR images and
corresponding to the weight of the T1, flair, T1C, and T2 images. The convolution is summed between
the different modalities of the MR images and its corresponding weight of the different modalities
of the MR images to obtain a new composite image which is given as an input image to
the deep convolutional neural network. The deep convolutional neural network performs segmentation
through the different layers of CNN and different filter operations are performed in each
layer to obtain the enhanced classification and segmented spatial consistency results. The analysis
of the proposed method shows that the discriminatory information from the different modalities
is effectively combined to increase the overall accuracy of segmentation.
Results: The proposed deep convolutional neural network for brain tumor segmentation method
has been analysed by using the Brain Tumor Segmentation Challenge 2013 database (BRATS
2013). The complete, core and enhancing regions are validated with Dice Similarity Coefficient
and Jaccard similarity index metric for the Challenge, Leaderboard, and Synthetic data set. To
evaluate the classification rates, the metrics such as accuracy, precision, sensitivity, specificity,
under-segmentation, incorrect segmentation and over segmentation also evaluated and compared
with the existing methods. Experimental results exhibit a higher degree of precision in the segmentation
compared to existing methods.
Conclusion: In this work, deep convolution neural network with different modalities of MR image
are used to detect the brain tumor. The new input image was created by convoluting the input
image of the different modalities and their weights. The weights are determined using the standard
deviation of the block. Segmentation accuracy is high with efficient appearance and spatial consistency.
The assessment of segmented images is completely evaluated by using well-established
metrics. In future, the proposed method will be considered and evaluated with other databases and
the segmentation accuracy results should be analysed with the presence of different kind of noises.