Back to Search
Start Over
Transformer-Based Approach to Pathology Diagnosis Using Audio Spectrogram.
- Source :
- Information (2078-2489); May2024, Vol. 15 Issue 5, p253, 18p
- Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20782489
- Volume :
- 15
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Information (2078-2489)
- Publication Type :
- Academic Journal
- Accession number :
- 177491357
- Full Text :
- https://doi.org/10.3390/info15050253