A Novel Machine Learning Approach of Hemorrhage Stroke Detection in Differential Microwave Head Imaging System

Mohammad Ojaroudi (1) , Stéphane Bila (1) , M. Salimitorkamani

1 XLIM-SRF - Systèmes RF

In this paper, brain hemorrhage stroke detection approach using microwave-imaging system with a novel machine-learning based post-processing method is presented. In order to create a circular array based microwave imaging system sixteen elements of the modified bowtie antennas are simulated in CST medium around the full head phantom. In order to radiate in desired band from 0.5-5 GHz an appropriate matching medium is designed. In addition, a hierarchical preprocessing method is employed to calibrate the reflected signals. In the processing section, a confocal imagereconstructing algorithm based is used. Finally, a new machine learning technique including discrete wavelet transform (DWT) and principle component analysis (PCA) for feature extraction and reduction, respectively. In addition, support vector machine (SVM) is used for segmentation and clustering of hemorrhage stroke detection from reconstructed image is employed. Simulated results are presented to validate the effectiveness of the proposed method for precisely localizing and classifying bleeding targets.

Mots clés: microwave brain imaging system; UWB matched bowtie antenna; confocal image reconctruction algorithm; haemorrhage stroke detection; segmentation and clustering.

Domaines: Electronique

Mohammad Ojaroudi, Stéphane Bila, M. Salimitorkamani. A Novel Machine Learning Approach of Hemorrhage Stroke Detection in Differential Microwave Head Imaging System. 2020 European Conference on Antennas and Propagation, Mar 2020, Copenhaguen, Denmark. ⟨hal-03047739⟩