{"id":4348,"date":"2021-01-22T15:56:36","date_gmt":"2021-01-22T14:56:36","guid":{"rendered":"https:\/\/www.eledia.org\/eledia-unitn\/?post_type=news&p=4348"},"modified":"2022-01-13T10:51:01","modified_gmt":"2022-01-13T09:51:01","slug":"new-electronics-publication-on-real-time-human-brain-stroke-diagnosis","status":"publish","type":"news","link":"https:\/\/www.eledia.org\/eledia-unitn\/news\/new-electronics-publication-on-real-time-human-brain-stroke-diagnosis\/","title":{"rendered":"New Electronics Publication on Real-Time Human Brain Stroke Diagnosis"},"content":{"rendered":"\n

The ELEDIA Research Center is pleased to announce that a new paper has been published in the Electronics<\/em> – Special Issue on \u2018New Trends and Future Challenges in Computational Microwave Imaging\u2019:<\/p>\n\n\n\n

M. Salucci, A. Polo, and J. Vrba, \u201cMulti-step learning-by-examples strategy for real-time brain stroke microwave scattering data inversion,\u201d Electronics \u2013 Special Issue on ‘New Trends and Future Challenges in Computational Microwave Imaging’, vol. 10, no. 1, pp. 1-17, January 2021 (DOI: 10.3390\/electronics10010095).<\/strong><\/p>\n\n\n\n

Abstract:<\/strong><\/p>\n\n\n\n

This work deals with the computationally-efficient inversion of microwave scattering data for brain stroke detection and monitoring. The proposed multi-step approach is based on the Learning-by-Examples (LBE) paradigm and naturally matches the stages and time constraints of an effective clinical diagnosis. Stroke detection, identification, and localization are solved with real-time performance through support vector machines (SVMs) operating both in binary\/multi-class classification and in regression modalities. Experimental results dealing with the inversion of laboratory-controlled data are shown to verify the effectiveness of the proposed multi-step LBE methodology and prove its suitability as a viable alternative\/support to standard medical diagnostic methods.<\/p>\n\n\n\n

The paper can be downloaded at the following link https:\/\/doi.org\/10.1109\/LAWP.2020.3010410<\/a>
Contact us at contact@eledia.org for the pre-print version of the manuscript.<\/p>\n\n\n\n