Smart Grid to Monitor Breast Cancer Patient Status

Subject Areas : Electronics Engineering

Mohammad Ali Pourmina ¹ , Javad Nouri Pour ² , Mohammad Naser-Moghaddasi ³ , Behbod Ghalamkari ⁴

1 - Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran
2 - Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran
3 - Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran
4 - Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran

Received: 2022-04-21 Accepted : 2022-07-08 Published : 2022-12-22

Keywords: communication weight, breast tumor, network capability arrangement, Nodes,

Abstract :

Immediate monitoring of the patient has always been very important. Achieving this knowledge, which can be integrated to monitor damaged tissue, is very important. In previous methods, the tissue was monitored using a sensor. In this article, not only is a tissue monitored using a sensor, but also the monitoring and evaluation of the effect of other tissues on tumor tissue is evaluated. The smart grid discussed in this article is designed to monitor the condition of a patient with a breast tumor. The structure of the smart grid, given the weight of the communication paths between the nodes and the ability of the nodes, shows us a strong network to assess the patient's condition. As the patient's condition changes, the nodes and weights of the communication pathways change, indicating that there is important information in the network and helping specialists to better assess the condition of the disease. Network monitoring is such that the evaluator node continuously evaluates the tumor node, by changing the status of the tumor node, the status of other nodes and communication paths between them changes, the result of changes in the network by the node The evaluator is evaluated. The simulation results show that this network has the necessary intelligence to assess the patient's condition in adverse conditions.

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