Voice-AttentionNet: Voice-Based Multi-Disease Detection with Lightweight Attention-Based Temporal Convolutional Neural Network
Voice data contain a wealth of temporal and spectral information and can be a valuable resource for disease classification. However, traditional methods are often not effective in capturing the key features required for the classification of multiple disease classes. To address this challenge, we propose a voice-based multi-disease detection approach with a lightweight attention-based temporal convolution neural network (Voice-AttentionNet) designed to analyze speech data for multi-class disease classification. Our model utilizes the temporal convolution neural network (CNN) architecture to extract high-resolution temporal features, while incorporating attention mechanisms to highlight disease-related patterns. Extensive experiments have been conducted on our dataset, including speech samples from patients with multiple illnesses. The results show that our method achieves the most advanced performance with an average classification accuracy of 91.61% on six datasets and is superior to the existing classical models. These findings highlight the potential of combining attention mechanisms with temporal CNNs in the use of speech data for disease classification. Moreover, this study provides a promising direction for deploying AI-driven diagnostic tools in clinical scenarios.