Secure blockchain enabled Cyber- Physical health systems using ensemble convolution neural network classification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019638" target="_blank" >RIV/62690094:18470/22:50019638 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0045790622003172?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0045790622003172?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.compeleceng.2022.108058" target="_blank" >10.1016/j.compeleceng.2022.108058</a>

Result language
angličtina
Original language name
Secure blockchain enabled Cyber- Physical health systems using ensemble convolution neural network classification
Original language description
Breast cancer is the most widely recognized malignancy affecting women. The risk of death has been consistently associated with breast cancer. In addition, the cyber-physical system (CPS)is the processing and data transfer of physical processes. This study presents a safe, intrusive, blockchain-based data transfer using the CPS classification model in the health industry to overcome the problem. Considering the challenges in breast tumor classification, this paper accords a reasonable arrangement to examine the mammogram image to discover the detection and classification of various stages of cancer. The breast cancer detection images obtained from the mammogram were processed and experimentally evaluated for parameters such as a sensitivity of 90%, a specificity of 98%,and a classification accuracy of 98%.The results of the ensemble convolution neural network (E-CNN) classifier, such as VGG-16 and Inception-v3, which separates ordinary and unusual cases from the applied advanced mammographic image, will be projected by comparing the two existing methods.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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