Comparison of Deep Learning Models for Voice Disorder Classification Using Phonovibrographic Images
Year: 2025
Type: article
Abstract: 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.
Source: Image Analysis & Stereology
DOI: 10.5566/ias.3741
Authors: B Panchami, S Pravin Kumar
Institution: Sri Sivasubramaniya Nadar College of Engineering
Language: English
Topic: Voice and Speech Disorders
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $215
OpenAlex ID: W4416805683
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