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What is Machine Learning for Vocal Fold Analysis about?

This study presents a machine learning approach for vocal fold (VF) segmentation and disorder classification using ensemble methods, achieving high accuracy in both tasks. The EfficientNetV2L-LGBM model for classification reached a test accuracy of 97.88%, while the UNet-BiGRU model for segmentation achieved a test accuracy of 91.47%. This integrated system aims to enhance diagnostic accuracy and improve healthcare for individuals affected by VF disorders.

Author

Fahima Minda

Language

EN