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.
Real-time brain stroke detection through a learning-by-examples technique – An experimental assessment / Salucci, M.; Vrba, J.; Merunka, I.; Massa, and A.. - In: MICROWAVE AND OPTICAL TECHNOLOGY LETTERS. - ISSN 1098-2760. - STAMPA. - 59:11(2017), pp. 2796-2799. [10.1002/mop.30821]
Real-time brain stroke detection through a learning-by-examples technique – An experimental assessment
M. Salucci;and A. Massa
2017-01-01
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.File | Dimensione | Formato | |
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