Cell-Free DNA Pattern Identification and its Classification using Convolution Neural Network
Nithya.P1, Arthy.P.S2, Prabhu Kumar.S3
1Nithya.P*, Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai, India.
2Arthy.P.S, Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, India.
3Prabhu Kumar.S, Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai, India.
Manuscript received on October 17, 2019. | Revised Manuscript received on 28 October, 2019. | Manuscript published on November 10, 2019. | PP: 5362-5364 | Volume-9 Issue-1, November 2019. | Retrieval Number: L37021081219/2019©BEIESP | DOI: 10.35940/ijitee.L3702.119119
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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: Circulating cell DNA (cfDNA) design identification assumes a cardinal job in fetal drug, transplantation and oncology. Be that as it may, it has additionally demonstrated to be a biomarker for different maladies. There are numerous order strategies by which the acknowledgment and arrangement should be possible. So as to have a superior time unpredictability and improve the precision further, this strategy targets distinguishing and arranging the general DNA examples and ailments related with them utilizing cfDNA Images in a Convolution Neural Network. A probabilistic method is used for cfDNA image feature extraction, fragmentation and interpretation. Graphical User Interface is the platform where this method is employed since it uses visual indicators in place of text-based interface. The aftereffects of this test demonstrate that the Convolution Neural Network calculation can perceive cfDNA successions accurately and successfully with no dubiety. Prepared with examples, the CNN can effectively characterize the picture surrendered to coordinated and unparalleled examples with numerical highlights.
Keywords: cfDNA, Convolution Neural Network, Graphical User Interface, GLCM Feature Extraction, Bioinformatics.
Scope of the Article: Classification