• E MURALI
• Dr. K MEENA

Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Tumor detection is an important task. Mask-RCNN is a recently proposed state-of-the-art algorithm for object detection, object localization, and object instance segmentation of MRI images. This paper explores segmenting brain tumor using two methods. The first method uses a cascade of a WNet and a UNet and the second method uses a Mask R-CNN framework to classify tumors in the brain. In this paper we demonstrate that Mask-RCNN can be used to perform highly effective and efficient automatic segmentations of a wide range of microscopy images.

Brian Tumor, Mask R-CNN, WNet, UNet, Stochastic gradient descent

"Stochastic Gradient Descent Optimizer for Segmentation of Brain Tumor using Mask R-CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.234-238, June-2019, Available :http://www.jetir.org/papers/JETIR1907E34.pdf

"Stochastic Gradient Descent Optimizer for Segmentation of Brain Tumor using Mask R-CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp234-238, June-2019, Available at : http://www.jetir.org/papers/JETIR1907E34.pdf