Medical Image Intelligent Recognition Predicts the Recessivity Variation of Human Tissue

Zhu Rongrong

doi:doi:10.11648/j.ajam.20200802.12

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Medical Image Intelligent Recognition Predicts the Recessivity Variation of Human Tissue

Zhu Rongrong

Published in American Journal of Applied Mathematics (Volume 8, Issue 2)

Received: 7 January 2020 Accepted: 10 February 2020 Published: 24 February 2020

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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.

Published in	American Journal of Applied Mathematics (Volume 8, Issue 2) This article belongs to the Special Issue Molecular Cellular Information Mathematics-Differential Incremental Equilibrium Geometry
DOI	10.11648/j.ajam.20200802.12
Page(s)	51-63
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Heavy Nuclear Clustering, Image Entropy, Cancer Cell Accumulation, Cell Modification, Tissue Recessive Variation, Intelligent Recognition

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Zhu Rongrong. (2020). Medical Image Intelligent Recognition Predicts the Recessivity Variation of Human Tissue. American Journal of Applied Mathematics, 8(2), 51-63. https://doi.org/10.11648/j.ajam.20200802.12

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ACS Style

Zhu Rongrong. Medical Image Intelligent Recognition Predicts the Recessivity Variation of Human Tissue. Am. J. Appl. Math. 2020, 8(2), 51-63. doi: 10.11648/j.ajam.20200802.12

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AMA Style

Zhu Rongrong. Medical Image Intelligent Recognition Predicts the Recessivity Variation of Human Tissue. Am J Appl Math. 2020;8(2):51-63. doi: 10.11648/j.ajam.20200802.12

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@article{10.11648/j.ajam.20200802.12,
  author = {Zhu Rongrong},
  title = {Medical Image Intelligent Recognition Predicts the Recessivity Variation of Human Tissue},
  journal = {American Journal of Applied Mathematics},
  volume = {8},
  number = {2},
  pages = {51-63},
  doi = {10.11648/j.ajam.20200802.12},
  url = {https://doi.org/10.11648/j.ajam.20200802.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20200802.12},
  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.},
 year = {2020}
}

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TY  - JOUR
T1  - Medical Image Intelligent Recognition Predicts the Recessivity Variation of Human Tissue
AU  - Zhu Rongrong
Y1  - 2020/02/24
PY  - 2020
N1  - https://doi.org/10.11648/j.ajam.20200802.12
DO  - 10.11648/j.ajam.20200802.12
T2  - American Journal of Applied Mathematics
JF  - American Journal of Applied Mathematics
JO  - American Journal of Applied Mathematics
SP  - 51
EP  - 63
PB  - Science Publishing Group
SN  - 2330-006X
UR  - https://doi.org/10.11648/j.ajam.20200802.12
AB  - 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.
VL  - 8
IS  - 2
ER  -

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Author Information

Zhu Rongrong

Differential Incremental Equilibrium Geometry, Mathematics Research of HR, Fudan University, Shanghai, China

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APA Style

Zhu Rongrong. (2020). Medical Image Intelligent Recognition Predicts the Recessivity Variation of Human Tissue. American Journal of Applied Mathematics, 8(2), 51-63. https://doi.org/10.11648/j.ajam.20200802.12

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ACS Style

Zhu Rongrong. Medical Image Intelligent Recognition Predicts the Recessivity Variation of Human Tissue. Am. J. Appl. Math. 2020, 8(2), 51-63. doi: 10.11648/j.ajam.20200802.12

Copy | Download

AMA Style

Zhu Rongrong. Medical Image Intelligent Recognition Predicts the Recessivity Variation of Human Tissue. Am J Appl Math. 2020;8(2):51-63. doi: 10.11648/j.ajam.20200802.12

Copy | Download

@article{10.11648/j.ajam.20200802.12,
  author = {Zhu Rongrong},
  title = {Medical Image Intelligent Recognition Predicts the Recessivity Variation of Human Tissue},
  journal = {American Journal of Applied Mathematics},
  volume = {8},
  number = {2},
  pages = {51-63},
  doi = {10.11648/j.ajam.20200802.12},
  url = {https://doi.org/10.11648/j.ajam.20200802.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20200802.12},
  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.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Medical Image Intelligent Recognition Predicts the Recessivity Variation of Human Tissue
AU  - Zhu Rongrong
Y1  - 2020/02/24
PY  - 2020
N1  - https://doi.org/10.11648/j.ajam.20200802.12
DO  - 10.11648/j.ajam.20200802.12
T2  - American Journal of Applied Mathematics
JF  - American Journal of Applied Mathematics
JO  - American Journal of Applied Mathematics
SP  - 51
EP  - 63
PB  - Science Publishing Group
SN  - 2330-006X
UR  - https://doi.org/10.11648/j.ajam.20200802.12
AB  - 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.
VL  - 8
IS  - 2
ER  -

Copy | Download