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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

COMPUTERS &amp; ELECTRICAL ENGINEERING

ISSN

0045-7906

e-ISSN

1879-0755

Svazek periodika

101

Číslo periodika v rámci svazku

JUL 2022

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

12

Strana od-do

"Article Number: 108058"

Kód UT WoS článku

000849743000007

EID výsledku v databázi Scopus

2-s2.0-85129691568