Enhancing the Accuracy of Image Classification for Degenerative Brain Diseases with CNN Ensemble Models Using Mel-Spectrograms

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.