Comparative Study of Machine Learning Approaches in Detection of Facial & Vocal Paralysis

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dc.contributor.advisorZunayeed Bin Zahir
dc.contributor.authorNazia Tabassum Toma
dc.contributor.authorFarishta Jayas Kinjol
dc.contributor.authorSyed Maaher Hossain
dc.contributor.id1721536042
dc.contributor.id1721411042
dc.contributor.id1731045042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025-08-18
dc.date.accessioned2025-08-18T05:17:16Z
dc.date.available2025-08-18T05:17:16Z
dc.date.issued2021
dc.description.abstractStatistical analysis has shown a significant increase in the rate of brain strokes in recent times. The time required to carry out the conventional methods to detect brain stroke on a patient requires both trained personal and time. Any discrepancy in the process might also lead to fatal consequences. To make the stroke detection technique less formidable, this report presents a method that minimizes the amount of time taken to detect stroke in a patient applying the FAST system coupled with a deep learning model that detects facial drooping and speech paralysis. We have compared different pre-trained models to find out the best suitable one for detecting vocal and facial paralysis. In our findings, we successfully ran tests on facial image and voice recording datasets. We were able to attain 99.67% training accuracy and a 99.37% validation accuracy using the VGG-16 deep learning model for facial paralysis and 73.02% training accuracy and 76.86% validation accuracy using the ANN deep learning model for vocal paralysis.
dc.description.degreeUndergraduate
dc.identifier.cd600000630
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1378
dc.language.isoen
dc.publisherNorth South University
dc.rights© NSU Library
dc.titleComparative Study of Machine Learning Approaches in Detection of Facial & Vocal Paralysis
dc.typeThesis
oaire.citation.endPage90
oaire.citation.startPage1
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