A brain anomaly localization algorithm in an unsupervised machine learning (ML) framework is presented for electromagnetic brain imaging. The method is based on expected value estimation and takes the advantage of the highly symmetrical human brain. The algorithm processes signals collected from pairs of antennas that are positioned symmetrically around the head, discretizes the imaging domain into pixels, and computes the statistical fields between the antennas on the left and right sides of the head. Then, it concatenates their intensities along the axis normal to the imaging domain to compute the expected value for every pixel. The computed expected values are merged into a matrix containing expected values for all pixels. Pixels with higher intensity show the likelihood of an anomaly being present at that location. The assumption on brain symmetry from the electromagnetic perspective was tested on healthy volunteers using a 14-element array system with a working frequency band of 0.5 - 2.0 GHz. The obtained average similarity is 92% and it confirms the validity of the assumption. The same system is used to test the algorithm on different scenarios in simulations and experiments using realistic 3D head phantoms designed based on MRIs of real patients. The imaging results demonstrate the capability of the proposed algorithm to localize bleeding and estimate its size with less than 10% error in less than a minute, which makes it suitable for real-time use in emergency stroke scenarios.

Fatty Liver Disease (FLD) is becoming prevalent disease in nowadays lifestyle while not being restricted to individuals with uncontrolled alcohol intake. Early diagnosis can prevent advanced irreversible liver disease, liver failure and ultimately can save lives. Computational methods reduce the operator dependability and hence improve diagnostic reliability. Most of these methods are based on automated analysis of Computed Tomography (CT), Ultrasound (US), and Magnetic Resonance (MRI) images. Besides the high costs and harmful radiation involved in the conventional imaging tools, the outcome of the automated imaging strongly depends on the image quality. To address the shortcomings of current tools, we propose an electromagnetic system, including an antenna operating across the band 0.4-1 GHz as a data acquisition device and a supervised Machine Learning (ML) framework to learn an inferring model for FLD directly from collected data. This paper reports the system configuration, ML problem setup and the obtained results, which show an accuracy of more than 97% for the simulated torso model.