  One of the main requirements of tumor extraction is the annotation and
segmentation of tumor boundaries correctly. For this purpose, we present a
threefold deep learning architecture. First classifiers are implemented with a
deep convolutional neural network(CNN) andsecond a region-based convolutional
neural network (R-CNN) is performed on the classified images to localize the
tumor regions of interest. As the third and final stage, the concentratedtumor
boundary is contoured for the segmentation process by using the
Chan-Vesesegmentation algorithm. As the typical edge detection algorithms based
on gradients of pixel intensity tend to fail in the medical image segmentation
process, an active contour algorithm defined with the level set function is
proposed. Specifically, Chan- Vese algorithm was applied to detect the tumor
boundaries for the segmentation process. To evaluate the performance of the
overall system, Dice Score,Rand Index (RI), Variation of Information (VOI),
Global Consistency Error (GCE), Boundary Displacement Error (BDE), Mean
absolute error (MAE), and Peak Signal to Noise Ratio (PSNR) werecalculated by
comparing the segmented boundary area which is the final output of the
proposed, against the demarcations of the subject specialists which is the gold
standard. Overall performance of the proposed architecture for both glioma and
meningioma segmentation is with average dice score of 0.92, (also, with RI of
0.9936, VOI of 0.0301, GCE of 0.004, BDE of 2.099, PSNR of 77.076 and MAE of
52.946), pointing to high reliability of the proposed architecture.
