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https://github.com/franco-ruggeri/covid19-detection
Explainable detection of COVID-19 from chest X-ray images
https://github.com/franco-ruggeri/covid19-detection
computer-vision covid-19 deep-learning
Last synced: 2 months ago
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Explainable detection of COVID-19 from chest X-ray images
- Host: GitHub
- URL: https://github.com/franco-ruggeri/covid19-detection
- Owner: franco-ruggeri
- License: gpl-3.0
- Created: 2020-05-06T22:02:40.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-08-24T16:58:16.000Z (over 3 years ago)
- Last Synced: 2024-11-08T09:13:46.056Z (2 months ago)
- Topics: computer-vision, covid-19, deep-learning
- Language: Python
- Homepage:
- Size: 6.13 MB
- Stars: 3
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Explainable Detection of COVID-19 from Chest X-Ray Images
This project is part of the Deep Learning in Data Science course (DD2424) at KTH. The goal is to train a classifier for COVID-19 detection from chest X-ray (CXR) images and boost it with explainability. More information can be found in the [report](https://github.com/franco-ruggeri/dd2424-covid19-detection/blob/master/docs/report.pdf). Also check out our [presentation](https://www.youtube.com/watch?v=c1TNhvAmddE&feature=youtu.be).
This package provides:
- An [application](#2-application) with a graphical user interface (GUI). This application can be used to make predictions on your images using trained models.
- A [suite of tools](#2-command-line-suite) with a command-line interface (CLI). These tools can be used to train and test new models.
- Several modules with a Keras-like API. These modules can be used in Python code.
# 1. Setup
The recommended installation is the following:
```
wget https://raw.githubusercontent.com/franco-ruggeri/dd2424-covid19-detection/master/scripts/install.sh -O install.sh
bash -i install.sh
```
Following the prompt, you can get a ready-to-use installation that uses the [best models we trained](https://drive.google.com/drive/folders/1x7_xh1xNcuvT8j29y7pTyk_3nrFHNZd2?usp=sharing).
The package is distributed on [PyPi](https://pypi.org/), so can be installed also with:
```
pip install covid19-detection
```
However, in this case you have to provide the trained models to the application. You can decide either to download the [best models we trained](https://drive.google.com/drive/folders/1x7_xh1xNcuvT8j29y7pTyk_3nrFHNZd2?usp=sharing) or to train your own models with the [command-line tools](3-command-line-suite).
## 2. Application
If you have done the recommended installation, you can launch the application by searching it among the applications. Otherwise, you can launch it from the terminal:
```
covid19-detector
```
## 3. Command-line suite
The command-line suite is available under the *covid19-detection* command. It provides several subcommands. The list can be retrieved with:
```
covid19-detection -h
```
More information about each subcommand can be obtained with:
```
covid19-detection -h
```
## 4. Package
You can import the package in your Python code with:
```
import covid19
```
The covid19 package is composed of the following sub-packages:
- covid19.datasets: contains utilities for generating COVIDx, HAM10000 and for building an input pipeline with tf.data.
- covid19.models: contains ResNet50 and COVID-Net, two deep convolutional neural networks.
- covid19.explainers: contains Grad-CAM and IG, two explainable AI methods, with some utilities for plotting the explanations.
- covid19.layers: contains layers used by models in covid19.models.
- covid19.metrics: contains utilities for computing and plotting metrics.
- covid19.gui: contains graphical user interface implemented with [Qt](https://www.qt.io/).
- covid19.cli: contains command-line interface.
Each subpackage provides interesting modules. For example, you can create a COVID-Net as follows:
```
from covid19.models import COVIDNet
model = COVIDNet(n_classes=3)
```
For more information about each class, see the comments.