ANALYSIS OF BRAIN MR IMAGES FOR TUMOUR DETECTION

Electronics and Telecommunications

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 tu- mors, which have 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 signif- icant variations in its structure. An automated brain tumor segmentation algorithm using deep convolu- tional 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 dropout, batch normalization, non-linear activation and inception module are used to build a new ILinear nexus architecture. The mod- ule overcomes the over-fitting problem arising due to scarcity of data using dropout 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 opera- tors are used for post-processing to remove small false positives around the edges.

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