Comparison of Deep Learning Models for Voice Disorder Classification Using Phonovibrographic Images

Accurate diagnosis of vocal fold disorders is difficult because of subtle variations between pathological conditions. Phonovibrography (PVG), generated from high-speed videoendoscopy (HSV), documents glottal vibration patterns as static images, allowing systemic analysis. In our study, we propose PVGNet, a hybrid deep learning model combining multiscale feature extraction and channel attention, designed specifically for PVG-based classification. We benchmark PVGNet against InceptionResNetV2, VGG19, DenseNet169, and X-ViT across binary, tertiary, and multi-class tasks. PVGNet continuously outperforms baselines in accuracy, F1-score, and AUC, by minimizing false negatives, which is important for reliable diagnosis. These results show PVG’s potential as a diagnostic imaging modality and PVGNet’s effectiveness in automated voice disorder classification.