Security Based Brain Tumor Classification using Image Fusion
M. SreeKrishna1, V. Rohini2, D. S. Premkumar3, N. Sankarram4, S. Gnanavel5
1M. Sreekrishna*, Assistant Professor, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, India.
2Rohini V, Department of Computer science and Engineering, Rajalakshmi Engineering College, Chennai, India.
3Premkumar D. S, Department of Computer science and Engineering, Rajalakshmi Engineering College, Chennai, India.
4N. Sankarram, Professor/Head, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, India.
5Dr. S. Gnanavel, Assistant Professor, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, India.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 30, 2020. | Manuscript published on May 10, 2020. | PP: 1315-1318 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5939059720/2020©BEIESP | DOI: 10.35940/ijitee.G5939.059720
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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: Today, the use of medical images is often complicated for diagnosis process and planning of treatment. The major challenge in image processing and fusion includes data mismatching, data storage issues and security constraints. Although several techniques are being used for image processing, they lack in security parameters. Our objective is to provide an efficient method for image fusion techniques along with the security paradigms. In order to provide security, encryption standards are used. The results of improved framework give better performance and quality over existing methods in terms of security, database information, and fusion factor.
Keywords: Tumor, Fusion, Security, Neural network.
Scope of the Article: Classification