Recognization and Systematization of MR Imagesusing K Means Clustering and DNN
Deepa. R1, Ashlin Lifty. S2, Deepigka. M. S3
1R. Deepa, M. E, Head Of Department, Computer Science Engineering, Prince Dr K. Vasudevan College of Engineering And Technology, Ponmar, Chennai, India.
2S. Ashlin Lifty, U.G Graduate, Computer Science Engineering, Prince Dr K. Vasudevan College of Engineering and Technology, Ponmar, Chennai, India.
3M. S. Deepigka, U. G Graduate, Computer Science Engineering, Prince Dr K. Vasudevan College of Engineering and Technology, Ponmar, Chennai, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 30, 2020. | Manuscript published on April 10, 2020. | PP: 924-927 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3811049620/2020©BEIESP | DOI: 10.35940/ijitee.F3811.049620
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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: Brain tumors are the result of unusual growth and unrestrained cell disunity in the brain. Most of the medical image application lack in segmentation and labeling. Brain tumors can lead to loss of lives if they are not detected early and correctly. Recently, deep learning has been an important role in the field of digital health. One of its action is the reduction of manual decision in the diagnosis of diseases specifically brain tumor diagnosis needs high accuracy, where minute errors in judgment may lead to loss therefore, brain tumor segmentation is an necessary challenge in medical side. In recent time numerous ,methods exist for tumor segmentation with lack of accuracy. Deep learning is used to achieve the goal of brain tumor segmentation. In this work, three network of brain MR images segmentation is employed .A single network is compared to achieve segmentation of MR images using separate network .In this paper segmentation has improved and result is obtained with high accuracy and efficiency.
Keywords: Deep Neural Network, K Means Clustering, Median Filtering, Histogram Equalization
Scope of the Article: Clustering