  Deep learning algorithms have accounted for the rapid acceleration of
research in artificial intelligence in medical image analysis, interpretation,
and segmentation with many potential applications across various sub
disciplines in medicine. However, only limited number of research which
investigates these application scenarios, are deployed into the clinical sector
for the evaluation of the real requirement and the practical challenges of the
model deployment. In this research, a deep convolutional neural network (CNN)
based classification network and Faster RCNN based localization network were
developed for brain tumor MR image classification and tumor localization. A
typical edge detection algorithm called Prewitt was used for tumor segmentation
task, based on the output of the tumor localization. Overall performance of the
proposed tumor segmentation architecture, was analyzed using objective quality
parameters including Accuracy, Boundary Displacement Error (BDE), Dice score
and confidence interval. A subjective quality assessment of the model was
conducted based on the Double Stimulus Impairment Scale (DSIS) protocol using
the input of medical expertise. It was observed that the confidence level of
our segmented output was in a similar range to that of experts. Also, the
Neurologists have rated the output of our model as highly accurate
segmentation.
