https://github.com/ankilab/airwaysymptomdetection

https://github.com/ankilab/airwaysymptomdetection

Repository

https://github.com/ankilab/AirwaySymptomDetection/blob/main/

# AirwaySymptomDetection

This repository contains related code to the paper below.


## How to use the code

To use the code, you need a Python installation together with relevant libraries (librosa, numpy, pandas, flammkuchen, scikit-learn, tensorflow).


## Overview 

![image](https://user-images.githubusercontent.com/62075292/146793807-ed3e3173-0bb3-438b-b9a1-b74315d47b60.png)


## Data preprocessing (Dataset creation)

Data preprocessing can be found in `neural_networks/src/dataloader.py`. Part of it is generating Mel-spectrograms from 1-D microphone-acoustic and mechano-acoustic data.


## Training deep neural networks (DNN mining)

We provide code to train several deep neural network architectures (`neural_networks`), e.g., ResNet, EfficientNet or RNNs. In `analysis`, you find a Jupyter notebook for evaluating the trained models. 

The file `neural_neworks/params.json` offers the possibility to specify various hyperparameters, especially with regard to the preprocessing of the data.

Aditionally, the repository provides code for training an autoencoder architecture (`neural_networks`) for converting from microphone-acoustic to mechano-acoustic Mel-spectrograms.


## Evolutionary optimization for wearable deployment

We provide code to run a genetic algorithm (`neural_networks/GeneticAlgorithm`) to optimize and find a low-size, accurate deep neural network architecture. 


## Genetic Algorithm model's performance on unseen data and learning capabilities

In `analysis`, you find two Jupyter notebooks for evaluating the Objective 2 model, which was determined using the Genetic Algorithm. 

Used datasets: 
* [Lei et al., 2020](https://www.mdpi.com/2076-3417/10/3/1192) - `predict-new-dataset-mcgill.ipynb`
* [COUGHVID crowdsourcing dataset](https://zenodo.org/record/4048312#.YcCYJseZNnI) - `predict-cough-database.ipynb`


## Explainable AI for mining AI decisions

In `analysis`, you find Jupyter notebooks for visualizing the class activation maps and results from occlusion experiments.


## How to cite this code

Groh et al. "Efficient and Explainable Deep Neural Networks for Airway Symptom Detection in Support of Wearable Health Technology", 2021

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