Abstract
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.
Highlights
The use of microwave radiation for sensing the human body is an emerging technology and a promising alternative/support to well-established medical equipment, such as magnetic resonance imaging (MRI ) and computed tomography (CT) [1,2,3]
It is worth highlighting that the main contribution of this work over the existing literature and preliminary validations from the authors [22,23] consists in the following key aspects: (i) an innovative integrated multi-step diagnosis framework exploiting progressively acquired information on the monitored patient’s health status, (ii) a novel regression-based localization approach to yield accurate predictions of the location of a previously-detected brain stroke, (iii) practical guidelines on the setting of the main support vector machines (SVMs) parameters, as well as (iv) insights on the nature/behavior of real scattering data acquired in a controlled environment when dealing with both ischaemic and hemorrhagic stroke phantoms
Dealing with Step 1 (“Detection”), training sets of increasing size N have been generated by collecting data distributed between “empty” (i.e., L(ξn) = −1—no stroke is present in D) and “full” (i.e., L(ξn) = +1—a stroke is present in D) scenarios, randomly varying both position and type of stroke for the latter class
Summary
The use of microwave radiation for sensing the human body is an emerging technology and a promising alternative/support to well-established medical equipment, such as magnetic resonance imaging (MRI ) and computed tomography (CT) [1,2,3]. It is worth highlighting that the main contribution of this work over the existing literature and preliminary validations from the authors [22,23] consists in the following key aspects: (i) an innovative integrated multi-step diagnosis framework exploiting progressively acquired information on the monitored patient’s health status, (ii) a novel regression-based localization approach to yield accurate predictions of the location of a previously-detected brain stroke, (iii) practical guidelines on the setting of the main SVM parameters, as well as (iv) insights on the nature/behavior of real scattering data acquired in a controlled environment when dealing with both ischaemic and hemorrhagic stroke phantoms. Some concluding observations and remarks are drawn (Section 4)
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