COMPARATIVE ANALYSIS OF DEEP LEARNING ARCHITECTURES FOR PENETRATION AND ASPIRATION DETECTION IN VIDEOFLUOROSCOPIC SWALLOWING STUDIES

Comparative Analysis of Deep Learning Architectures for Penetration and Aspiration Detection in Videofluoroscopic Swallowing Studies

This study concentrates on machine learning, specifically deep learning techniques, to automatically detect the presence of aspiration or penetration in videofluoroscopic swallowing studies (VFSS).A comparative analysis is conducted on various deep learning architectures such as 2D Convolutional Neural Networks (2D-CNN), Long Short-Term Memory (LST

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