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- Title
Transformer-Based Approach to Pathology Diagnosis Using Audio Spectrogram.
- Authors
Tami, Mohammad; Masri, Sari; Hasasneh, Ahmad; Tadj, Chakib
- Abstract
Early detection of infant pathologies by non-invasive means is a critical aspect of pediatric healthcare. Audio analysis of infant crying has emerged as a promising method to identify various health conditions without direct medical intervention. In this study, we present a cutting-edge machine learning model that employs audio spectrograms and transformer-based algorithms to classify infant crying into distinct pathological categories. Our innovative model bypasses the extensive preprocessing typically associated with audio data by exploiting the self-attention mechanisms of the transformer, thereby preserving the integrity of the audio's diagnostic features. When benchmarked against established machine learning and deep learning models, our approach demonstrated a remarkable 98.69% accuracy, 98.73% precision, 98.71% recall, and an F1 score of 98.71%, surpassing the performance of both traditional machine learning and convolutional neural network models. This research not only provides a novel diagnostic tool that is scalable and efficient but also opens avenues for improving pediatric care through early and accurate detection of pathologies.
- Subjects
MACHINE learning; CONVOLUTIONAL neural networks; TRANSFORMER models; DEEP learning; ARTIFICIAL neural networks; SPECTROGRAMS; AUDIO equipment; POWER transformers
- Publication
Information (2078-2489), 2024, Vol 15, Issue 5, p253
- ISSN
2078-2489
- Publication type
Article
- DOI
10.3390/info15050253