  Detection of brain tumor using a segmentation based approach is critical in
cases, where survival of a subject depends on an accurate and timely clinical
diagnosis. Gliomas are the most commonly found tumors having irregular shape
and ambiguous boundaries, making them one of the hardest tumors to detect. The
automation of brain tumor segmentation remains a challenging problem mainly due
to significant variations in its structure. An automated brain tumor
segmentation algorithm using deep convolutional neural network (DCNN) is
presented in this paper. A patch based approach along with an inception module
is used for training the deep network by extracting two co-centric patches of
different sizes from the input images. Recent developments in deep neural
networks such as drop-out, batch normalization, non-linear activation and
inception module are used to build a new ILinear nexus architecture. The module
overcomes the over-fitting problem arising due to scarcity of data using
drop-out regularizer. Images are normalized and bias field corrected in the
pre-processing step and then extracted patches are passed through a DCNN, which
assigns an output label to the central pixel of each patch. Morphological
operators are used for post-processing to remove small false positives around
the edges. A two-phase weighted training method is introduced and evaluated
using BRATS 2013 and BRATS 2015 datasets, where it improves the performance
parameters of state-of-the-art techniques under similar settings.
