Real-Time Tumor Detection Using Electromagnetic Signals With Memristive Echo State Networks

Early detection and diagnosis of brain tumors are of great significance, as they can be life saving. Current state-of-the-art methods, including X-ray and magnetic resonance imaging (MRI) require more resources and advanced medical facilities, and cannot be used for continuous or long-term monitoring. The importance of this contribution lies in the timely detection of these medical conditions. In our work, we propose a method for identifying the brain tumors that overcomes these shortcomings. Two antennas,
${\mathrm { Ant}}_{1}$
and
${\mathrm { Ant}}_{2}$
were used around the head, and changes in the transmission coefficients
$(S_{21})$
were monitored. Experiments are conducted on a human head-shaped container, and the transmission data obtained were transferred to a memristor crossbar array using a voltage threshold adaptive memristor (VTEAM) model for the prediction of cancer. The proposed crossbar is used for implementing echo state networks that detects the presence of cancer with an accuracy of 77.5% after incorporating compensation for signal integrity influences.