Gene Microarray Analysis Using Fsom Methodology
Durga Prasad Kondisetty1, Mohammed Ali Hussain2
1Durga Prasad Kondisetty Research Scholar, Department of Computer Science, Bharathiar University, (Tamil Nadu), India.
2Mohammed Ali Hussain, Professor, Department of Electronics & Computer Science Engineering, KLEF, Guntur (A.P), India.
Manuscript received on 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 18-22 | Volume-8 Issue-5, March 2019 | Retrieval Number: D2760028419/19©BEIESP
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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: The self-organizing maps (SOM) is a supervised neural network(NN) studying technology but there is some data remaining to extract to analysis the neural network which has been frequently used for the analysis and organization of data files having a large size. In this same manner fuzzy c-means (FCM) is also a supervised methodology to segment the image. Here, introducing a novel path to deal with the consequent section of Magnetic Resonance (MR) imaging of the human brain into anatomical locales. This paper presents an analysis segmentation of microarray brain image in an unsupervised methodology by combines the supervised FCM and SOM methodologies.
Keyword: Image Segmentation, Fcm, Som, Microarray, Mri, Fsom.
Scope of the Article: Predictive Analysis