Development and Porting Of Mathematical Model to Design a PixTra Algorithm for Detection of Brain Tumor
Rahul Khalate1, Vineeta Basotia2, Dilip.B.Ghule3

1Mr.Rahul Khalate, Ph. D. Research Scholar, JJTU, Jhunjhunu, Rajasthan, India
2Dr. Vineeta Basotia, Assistant Professor, Department of Mathematics, JJTU University, Rajasthan India.
3Dr .Dilip. B. Ghule, Assistant Professor, Department of Mathematics, ESD College Varvand, SPPU University, Pune India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2030-2032 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9549109119/2019©BEIESP | DOI: 10.35940/ijeat.A9549.109119
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Abstract: The goal of applied mathematics is to develop and implement the automated systems, services and support to execute engineering goals. Game theory is always a domain of choice for development of strategy to enhance the usability of mathematical applications. Today, there is rarely any system which works without keeping mathematics behind the curtain. A recent trend in medical imaging leads via mathematical algorithmic development for brain tumor analysis. The deep learning technology is a mathematical strategic solution to identify the brain tumor from medical resonance imaging. The game theory strategic development is necessary for mathematical modeling of image technologies as pixel level calculations are required. Hence, this paper shows the newly developed mathematical modeling for efficient brain tumor detection using game theory and set theory. This paper also suggests the new algorithm which is ported from mathematical modeling for deep learning brain tumor image processing.
Keywords: Game Theory, Mathematical modeling, set theory, integral application, Markova Analysis, image processing, algorithm modeling.