Fully Connected Pyramid Pooling Network (FCPPN) – A Method For Brain Tumor Segmentation
S.Fathima Suhara1, M.Safish Mary2

1S.Fathima Suhara* Research Scholar, Manonmaniam Sundaranar University, Tirunelveli.
2Dr. M. Safish Mary Assistant Professor, Department of Computer Science, St. Xaviers College, Tirunelveli.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 7036-7041 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1658109119/2019©BEIESP | DOI: 10.35940/ijeat.A1658.109119
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The procedure of separating the tumor from ordinary cerebrum images is called as brain tumor Segmentation . In segmenting the tumor it allows us to visualize the size and position of tumor within the brain.In Manual segmentation there is less accuracy so there is a need for fully automatic segmentation. A fully automatic segmentation called Semantic segmentation is a technique that classifies all the pixels of an image into meaningful classes of objects. Semantic Segmentation is mainly used in the area of medical imaging. It is mainly used for the doctors to identify the tumor in a clear and exact way. In this paper, we propose a new way of semantic segmentation technique to separate the tumor from the brain . The methods like Segnet, FCN, PSPNET are used for fully automatic segmentation and are used to predicate all types of Tumor. These methods are used to predicate the tumor.Our paper proposes a new architecture called FCPPNET which is a hybrid combination of FCN and PSPNET. Our proposed strategy is assessed utilizing Performance measurements, for example, the Dice coefficient, Accuracy, Sensitivity, and the outcomes appear to be more productive than the current strategies.
Keywords: FCN-Fully connected network, PSPNET-Pyramid Scene Parsing Network, VGG-Visual Geometry Group, NCUT-Normalized Cut , PET-Positron Emission Tomography, MRS-Magnetic Resonance Spectroscopy.