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Deep Learning Classification of Operator and Endoscope Motions during Endoscopy

Rodriguez Ruiz, G. (2021) Deep Learning Classification of Operator and Endoscope Motions during Endoscopy.

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Abstract:This study aims at classifying operator and endoscope motions to identify the key movements necessary to perform upper diagnostic flexible endoscopy and to study the relation between operator actions and endoscope response during endoscopy procedures. Motion data of both operator and endoscope are simultaneously recorded during clinical endoscopy procedures. Classifying these motions is not trivial as different operators generate different motion data even during the same procedure. Due to the complexity of the problem, the classification of different motions will be tackled with Deep Learning.
Item Type:Essay (Master)
Clients:
IRCAD, Strasbourg, France
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:31 mathematics, 44 medicine, 54 computer science
Programme:Electrical Engineering MSc (60353)
Link to this item:https://purl.utwente.nl/essays/89240
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