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

Abstract

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 (LSTM), and 3D Convolutional Neural Networks (3D-CNN). This comparison assesses the performance, network size, and computational speed of the models. In addition, we present findings derived from multi-label and multi-class classification methods. By evaluating the strengths and weaknesses of each technique, we propose the most effective method for detecting penetration or aspiration in VFSS. Our comprehensive evaluation reveals the superiority of 3D-CNN in the automatic detection of penetration and aspiration in VFSS. This research contributes to the development of a clinically viable automatic detection system, offering potential advancements in the care and management of patients with dysphagia.