SIMULATION AND CONCEPTION OF MIMO BIOSENSOR FOR DAMAGE LUNGS RUNG CLASSIFICATION.

Abstract

In the aftermath of the COVID-19 pandemic, many individuals have suffered from severe health repercussions, including both short and long-term lung damage. While X-rays have traditionally been employed for detecting lung issues, the need for faster and more efficient diagnostic methods is evident. In response to this need, this study introduces a novel split ring resonator (SRR) as Multiple-Input Multiple-Output (MIMO) biosensor. It is designed for the millimeter range, to swiftly and safely detect pneumonia associated to the COVID-19. Operating within the 5G frequency bands (36 GHz to 38 GHz) and leveraging metamaterial technology, this biosensor offers a compact solution for identifying lung abnormalities. By analyzing the water percentage in the lungs, the MIMO biosensor distinguishes the lung damage’s levels. Through extensive neural network classification and MIMO biosensor’s S parameters, a robust model for accurately classifying lung damage is developed. The proposed MIMO biosensor device demonstrates precise detection of affected lung level.