Medical Image Intelligent Recognition Predicts the Recessivity Variation of Human Tissue
American Journal of Applied Mathematics
Volume 8, Issue 2, April 2020, Pages: 51-63
Received: Jan. 7, 2020; Accepted: Feb. 10, 2020; Published: Feb. 24, 2020
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Author
Zhu Rongrong, Differential Incremental Equilibrium Geometry, Mathematics Research of HR, Fudan University, Shanghai, China
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Abstract
By analyzing and predicting the latent variation of human tissues, the concept of iterative programming of heavy kernel clustering is introduced to solve the problem of intelligent recognition of medical images of inflammation and cancer. Inflammatory cells modify the accumulation of cancer cells and leap to the early stage of cancer, which is called image entropy. The hypercomplex symmetric structure of the edge sliding kernel of the entropy kernel of high-dimensional s≥ 6 image. As well as the fusion of image entropy nucleus dumbbell double sphere complex sphere, the exchangeability of the central source extreme compression line sink; the central source superstring sink compresses to the critical point, and the unconstrained 2N + 1 laminated incision will cause the high-dimensional superstring sink to break up and release the exfoliated cells. Non analytic exploitation is the inverse kernel factor of aidicom that can judge the entropy of latent tissue variation image from inflammation to early cancer. It is a foundation of revealing (predicting) system recognition data array, and can carry the first-order and second-order partial differential carriers of kernel core area. In the medical image, the identification of inflammation and cancer often troubles doctors. Based on the inherent logic between cell modification fluctuation and image, aidicom system gives the concept of image entropy, and uses the dieg algorithm to complete the classification of focus detection and recognition, as well as the prediction of future development.
Keywords
Heavy Nuclear Clustering, Image Entropy, Cancer Cell Accumulation, Cell Modification, Tissue Recessive Variation, Intelligent Recognition
To cite this article
Zhu Rongrong, Medical Image Intelligent Recognition Predicts the Recessivity Variation of Human Tissue, American Journal of Applied Mathematics. Special Issue: Molecular Cellular Information Mathematics-Differential Incremental Equilibrium Geometry . Vol. 8, No. 2, 2020, pp. 51-63. doi: 10.11648/j.ajam.20200802.12
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Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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