# An Ensemble of Deep Learning Frameworks Applied For Predicting Respiratory Anomalies

arXiv: https://arxiv.org/abs/2201.03054 · HTML: https://mlsanity.com/paper/2201.03054/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2022.

## Built on by (most cited first, 2 of 2)

- [An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomalies](https://mlsanity.com/paper/2303.04104/) (2023)
- [Variational Autoencoders for Anomaly Detection in Respiratory Sounds](https://mlsanity.com/paper/2208.03326/) (2022)

## Abstract (arXiv)

In this paper, we evaluate various deep learning frameworks for detecting respiratory anomalies from input audio recordings. To this end, we firstly transform audio respiratory cycles collected from patients into spectrograms where both temporal and spectral features are presented, referred to as the front-end feature extraction. We then feed the spectrograms into back-end deep learning networks for classifying these respiratory cycles into certain categories. Finally, results from high-performed deep learning frameworks are fused to obtain the best score. Our experiments on ICBHI benchmark dataset achieve the highest ICBHI score of 57.3 from a late fusion of inception based and transfer learning based deep learning frameworks, which outperforms the state-of-the-art systems.