Machine learning enters the world of medical application and, in this paper, it joins microwave imaging technique for brain stroke classification. One of the main challenges in this application is the need of a large amount of data for the machine learning algorithm training that can be performed via measurements or simulations. In this work, we propose to make the algorithm training via simulations based on a linear integral operator that reduces by three orders of magnitude the data generation time with respect to standard full-wave simulations. This method is used here to train the multilayer perceptron algorithm. The data-set is organized in nine classes, related to the presence, the type and the position of the stroke within the brain. We verified that the algorithm metrics (accuracy, recall and precision) reach values close to 1 for each class.

Simulation-based Machine Learning Training for Brain Anomalies Localization at Microwaves / Mariano, Valeria; Casu, Mario R.; Vipiana, Francesca. - ELETTRONICO. - (2022). (Intervento presentato al convegno 2022 16th European Conference on Antennas and Propagation (EuCAP) tenutosi a Madrid, Spain nel 27 March-1 April 2022) [10.23919/EuCAP53622.2022.9769504].

Simulation-based Machine Learning Training for Brain Anomalies Localization at Microwaves

Valeria Mariano;Mario R. Casu;Francesca Vipiana
2022

Abstract

Machine learning enters the world of medical application and, in this paper, it joins microwave imaging technique for brain stroke classification. One of the main challenges in this application is the need of a large amount of data for the machine learning algorithm training that can be performed via measurements or simulations. In this work, we propose to make the algorithm training via simulations based on a linear integral operator that reduces by three orders of magnitude the data generation time with respect to standard full-wave simulations. This method is used here to train the multilayer perceptron algorithm. The data-set is organized in nine classes, related to the presence, the type and the position of the stroke within the brain. We verified that the algorithm metrics (accuracy, recall and precision) reach values close to 1 for each class.
2022
978-88-31299-04-6
File in questo prodotto:
File Dimensione Formato  
1570770300 final.pdf

accesso aperto

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 203.92 kB
Formato Adobe PDF
203.92 kB Adobe PDF Visualizza/Apri
Da IEEE Simulation-based_Machine_Learning_Training_for_Brain_Anomalies_Localization_at_Microwaves.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 224.68 kB
Formato Adobe PDF
224.68 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2964071