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Human Lung Non-Invasive Anomaly Detection through UWB Microwave Imaging and Diagnosis of COVID-19: A Possible Application
3rd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2023 ; 2023-January:269-274, 2023.
Article in English | Scopus | ID: covidwho-2301053
ABSTRACT
This study shows a prototype for detecting lung effects using microwave imaging. Continuous monitoring of pulmonary fluid levels is one of the most successful approaches for detecting fluid in the lung;early Chest X-rays, computational tomography (CT)-scans, and magnetic resonance imaging (MRI) are the most commonly used instruments for fluid detection. Nonetheless, they lack sensitivity to ionizing radiation and are inaccessible to the general public. This research focuses on the development of a low-cost, portable, and noninvasive device for detecting Covid-19 or lung damage. The simulation of the system involved the antenna design, a 3D model of the human lung, the building of a COMSOL model, and image processing to estimate the lung damage percentage. The simulation consisted of three components. The primary element requires mode switching for four array antennas (transmit and receive). In the paper, microwave tomography was used. Using microwave near-field imaging, the second component of the simulation analyses the lung's bioheat and electromagnetic waves as well as examines the image creation under various conditions;many electromagnetic factors seen at the receiving device are investigated. The final phase of the simulation shows the affected area of the lung phantom and the extent of the damage. © 2023 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 3rd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2023 Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 3rd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2023 Year: 2023 Document Type: Article