Document Type
Article
Publication Date
2025
DOI
10.3390/math13132100
Publication Title
Mathematics
Volume
13
Issue
13
Pages
2100 (1-18)
Abstract
Alzheimer’s disease (AD) and Parkinson’s disease (PD) are prevalent neurodegenerative disorders among the elderly, leading to cognitive decline and motor impairments. As the population ages, the prevalence of these neurodegenerative disorders is increasing, providing motivation for active research in this area. However, most studies are conducted using brain imaging, with relatively few studies utilizing voice data. Using voice data offers advantages in accessibility compared to brain imaging analysis. This study introduces a novel ensemble-based classification model that utilizes Mel spectrograms and Convolutional Neural Networks (CNNs) to distinguish between healthy individuals (NM), AD, and PD patients. A total of 700 voice samples were collected under standardized conditions, ensuring data reliability and diversity. The proposed ternary classification algorithm integrates the predictions of binary CNN classifiers through a majority voting ensemble strategy. ResNet, DenseNet, and EfficientNet architectures were employed for model development. The experimental results show that the ensemble model based on ResNet achieves a weighted F1 score of 91.31%, demonstrating superior performance compared to existing approaches. To the best of our knowledge, this is the first large-scale study to perform three-class classification of neurodegenerative diseases using voice data.
Rights
© 2025 by the authors.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY 4.0) License.
Data Availability
Article states: "Data can be obtained at the following URL: https://github.com/sanghasung/Ensemble_CNN_Model (accessed on 24 June 2025)."
Original Publication Citation
Sung, S. H., Pokojovy, M., Kang, D. Y., Bae, W. Y., Hong, Y. J., & Kim, S. (2025). Enhancing the accuracy of image classification for degenerative brain diseases with CNN ensemble models using Mel-spectrograms. Mathematics, 13(13), 1-18, Article 2100. https://doi.org/10.3390/math13132100
ORCID
0000-0002-2122-2572 (Pokojovy)
Repository Citation
Sung, Sang-Ha; Pokojovy, Michael; Kang, Do-Young; Bae, Woo-Yong; Hong, Yeon-Jae; and Kim, Sangjin, "Enhancing the Accuracy of Image Classification for Degenerative Brain Diseases with CNN Ensemble Models Using Mel-Spectrograms" (2025). Mathematics & Statistics Faculty Publications. 294.
https://digitalcommons.odu.edu/mathstat_fac_pubs/294
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Artificial Intelligence and Robotics Commons, Disease Modeling Commons, Mathematics Commons, Neurology Commons