RT info:eu-repo/semantics/article
T1 Optimization of a Deep-Learning-Based Cough Detector Using eXplainable Artificial Intelligence for Implementation on Mobile Devices
A1 Amado Caballero, Patricia
A1 Varona Peña, Inés
A1 Gutiérrez García, Benjamín Gerardo
A1 Aguiar Pérez, Javier Manuel
A1 Rodríguez Cayetano, Manuel
A1 Gómez Gil, Jaime
A1 Garmendia Leiza, José Ramón
A1 Casaseca de la Higuera, Juan Pablo
K1 Enfermedades respiratorias
K1 Tos
K1 Análisis de audio
K1 CNN
K1 XAI
K1 Mapas de oclusión
K1 Optimización
AB Respiratory diseases, including COPD and cancer, are among the leading causes of mortality worldwide, often resulting in prolonged dependency and impairment. Telemedicine offers immense potential for managing respiratory diseases, but its effectiveness is hindered by the lack of reliable objective measures for symptoms. Recent advances in deep learning have significantly enhanced the detection and analysis of coughing episodes, a key symptom of respiratory conditions, by leveraging audio signals and pattern recognition techniques. This paper introduces an efficient cough detection system tailored for real-time monitoring on low-end computational devices, such as smartphones. By integrating Explainable Artificial Intelligence (XAI), we identify salient regions in audio spectrograms that are crucial for cough detection, enabling the design of an optimized Convolutional Neural Network (CNN). The optimized CNN maintains high detection performance while significantly reducing computation time and memory usage.
PB SciTePress
SN 2184-4305
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/78601
UL https://uvadoc.uva.es/handle/10324/78601
LA eng
NO SciTePress, 2025, 2, p. 491-498.
NO Producción Científica
DS UVaDOC
RD 08-dic-2025