Brain Tumor Segmentation using K-Means Clustering and Detection using Convolutional Neural Network
Prakash U. M.1, Satyam Pandey2, Khushbu Kumari3, P. Sathishkumar4
1Prakash U. M.*, Assistant Professor, Dept. of CSE, SRM Institute of Science and Technology, Chennai, India.
2Satyam Pandey, Dept. of CSE, SRM Institute of Science and Technology, Chennai, India.
3Khushbu Kumari, Dept. of CSE, SRM Institute of Science and Technology, Chennai, India.
4P. Sathishkumar, Assistant Professor, Dept. of CSE, SRM Institute of Science and Technology, Chennai, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on March 01, 2020. | Manuscript published on March 10, 2020. | PP: 1452-1455 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2855039520/2020©BEIESP | DOI: 10.35940/ijitee.E2855.039520
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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: This paper presents brain tumor detection and segmentation using image processing techniques. Convolutional neural networks can be applied for medical research in brain tumor analysis. The tumor in the MRI scans is segmented using the K-means clustering algorithm which is applied of every scan and the feed it to the convolutional neural network for training and testing. In our CNN we propose to use ReLU and Sigmoid activation functions to determine our end result. The training is done only using the CPU power and no GPU is used. The research is done in two phases, image processing and applying neural network.
Keywords: Convolutional Neural Network, Image processing, K-Means Clustering, ReLU rectifier.
Scope of the Article: Clustering