TY  - JOUR 
AU  - Jojoa Acosta, Mario Fernando
AU  - Bahillo Martínez, Alfonso
AU  - Arjona, Laura
AU  - Lorenzo Toledo, Rubén Mateo
AU  - Canelón, Elba
PY  - 2025
SN  - 0010-4825
UR  - https://uvadoc.uva.es/handle/10324/76982
AB  - The aim of this research is to help health care professionals to automatically detect lower urinary tract disorders using sounds of voiding recorded at home. In total 93 patients were diagnosed as obstructed or non-obstructed in a hospital using...
LA  - eng
PB  - Elsevier
KW  - Computer vision
KW  - Deep learning
KW  - Inception v3
KW  - Convolutional neural network
KW  - Scalogram
KW  - Wavelet
KW  - Low urinary tract symptoms
TI  - Comparison of three classifiers in detection of obstruction of the lower urinary tract using recorded sounds of voiding
DO  - 10.1016/j.compbiomed.2025.110337
ER  -