CBL - Campus del Baix Llobregat

Projecte llegit

Títol: Machine Learning techniques for microwave brain stroke detection and classification

Estudiants que han llegit aquest projecte:

Director/a: SERRANO FINETTI, ERNESTO

Departament: EEL

Títol: Machine Learning techniques for microwave brain stroke detection and classification

Data inici oferta: 09-05-2023     Data finalització oferta: 09-05-2023


Estudis d'assignació del projecte:
    GR ENG SIS TELECOMUN
Tipus: Individual
 
Lloc de realització: ERASMUS
 
Paraules clau:
Machine Learning, Stroke, SVM, MLP, Microwave Imaging
 
Descripció del contingut i pla d'activitats:
 
Overview (resum en anglès):
Strokes, defined by an interruption of the supply of oxygenated blood to the brain, are devastating brain injuries that can cause profound damage, temporary or permanent disability, or even death. It occurs when a brain blood vessel bursts (or ruptures) or becomes blocked by a clot. Stroke patients represent a serious medical emergency, and in order to increase the probability of recovery and lower the patient's damages, risk of death, or future disabilities, appropriate and timely diagnosis and treatment are crucial.

The main objective of this thesis is to investigate the use of machine learning (ML) techniques to detect and classify strokes. The system used is placed according to head phantoms, which have the same electrical characteristics as human head tissues at microwave frequencies. This project explores ways to improve the precision and efficacy of stroke detection and classification by applying machine learning's computational capabilities, ultimately with the goal of reducing the severe effects of this medical emergency.

Ultimately, this study represents an interesting development in the incorporation of machine learning into stroke diagnosis, with the potential to improve the accuracy and speed of diagnosis. It can be further developed by investigating alternative strategies such as improving classification methods, testing different algorithms, and calibrating simulations with measurements.
As an ongoing research activity, the continuing journey to improve stroke diagnosis and prediction will continue, motivated by reducing the life-changing effects of strokes.

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