Real-time brain stroke detection through a learning-by-examples technique-An experimental assessment

Abstract : The real‐time detection of brain strokes is addressed within the Learning‐by‐Examples (LBE) framework. Starting from scattering measurements at microwave regime, a support vector machine (SVM) is exploited to build a robust decision function able to infer in real‐time whether a stroke is present or not in the patient head. The proposed approach is validated in a laboratory‐controlled environment by considering experimental measurements for both training and testing SVM phases. The obtained results prove that a very high detection accuracy can be yielded even though using a limited amount of training data.
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Article dans une revue
Microwave and Optical Technology Letters, Wiley, 2017, 59 (11), pp.2796 - 2799. 〈10.1002/mop.30821〉
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https://hal.archives-ouvertes.fr/hal-01767538
Contributeur : Andrea Massa <>
Soumis le : lundi 16 avril 2018 - 12:06:03
Dernière modification le : mercredi 18 avril 2018 - 01:17:41

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Marco Salucci, Jan Vrba, Ilja Merunka, Andrea Massa. Real-time brain stroke detection through a learning-by-examples technique-An experimental assessment. Microwave and Optical Technology Letters, Wiley, 2017, 59 (11), pp.2796 - 2799. 〈10.1002/mop.30821〉. 〈hal-01767538〉

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