The Multi Stage U-net Design for Brain Tumor Segmentation using Deep Learning Architecture
Putta. Rama Krishna Veni1, C Aruna Bala2
1Putta. Rama Krishna Veni, Assistant Professor. Dept of ECE PNC vijai Institute of Eng & Technology KKR&KSR Institute of science and Technology. Guntur, Andrapradesh, India.
2Dr .C ArunaBala, Professor. Dept of ECE PNC vijai Institute of Eng & Technology KKR&KSR Institute of science and Technology. Guntur, Andrapradesh, India.
Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 454-456 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.C4531099320 | DOI: 10.35940/ijrte.C4531.099320
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© 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: Now a day’s diagnosis and accurate segmentation of brain tumors are critical conditions for successful treatment. The manual segmentation takes time consuming process, more cost and inaccurate. In this paper implementation of cascaded U-net segmentation Architecture are divided into substructures of brain tumor segmentation. The neural network is competent of end to end multi modal brain tumor segmentations.The Brain tumor segments are divided three categories. The tumor core (TC),the enhancing tumor(ET),the whole tumor (WT).The distinct data enhancement steps are better achievement. The proposed method can test result conclude average counter scores of 0.83268, 0.88797 and 0.83698, as well as Hausdorff distances 95%) of 2.65056, 4.61809 and 4.13071, for the enhancing tumor(ET), whole tumor (WT) and tumor core (TC) respectively. In this method validating with BraTS 2019 dataset and identify the test time enhancement improves the Brain tumor segmentation accurate images.
Keywords: Deep learning · Brain tumor segmentation · U-Net.