My PyTorch implementation of COVID-Net, for the original work please see: https://github.com/lindawangg/COVID-Net
The purpose of this github is to reproduce results and not to claim state-of-the-art performance !!
Also Google Colab Notebook for plug-n-play training and evaluation here -->IliasPap/covidnet.ipynb
To install the required python packages use the following command
pip install -r requirements.txt
The network takes as input an image of shape (N, 224, 224, 3) and outputs the softmax probabilities as (N, C), where N is the number of batches and C number of output classes.
- To train the Network from scratch simply do
python main.pyArguments for training
-h, --help show this help message and exit
--batch_size BATCH_SIZE
batch size foe training
--log_interval LOG_INTERVAL
steps to print metrics and loss
--dataset_name DATASET_NAME
dataset name
--nEpochs NEPOCHS total number of epochs
--device DEVICE gpu device
--seed SEED select seed number for reproducibility
--classes CLASSES dataset classes
--lr LR learning rate (default: 1e-3)
--weight_decay WEIGHT_DECAY
weight decay (default: 1e-6)
--cuda use gpu for speed-up
--tensorboard use tensorboard for loggging and visualization
--resume PATH path to latest checkpoint (default: none)
--model {COVIDNet_small,resnet18,mobilenet_v2,densenet169,COVIDNet_large}
--opt {sgd,adam,rmsprop}
--root_path ROOT_PATH
path to dataset
--save SAVE path to checkpoint save directory
with my implementation of COVID-Net and comparison with CNNs pretrained on ImageNet dataset
| Accuracy (%) | # Params (M) | MACs (G) | Model |
|---|---|---|---|
| 89.10 | 115.42 | 2.26 | [COVID-Net-Small] |
| 91.22 | 118.19 | 3.54 | COVID-Net-Large |
| 94.0 | - | - | Mobilenet V2 |
| 95.0 | - | - | ResNeXt50-32x4d |
| 94.0 | - | - | ResNet-18 |
Soon ...
| Accuracy (%) | # Params (M) | MACs (G) | Model |
|---|---|---|---|
| - | - | - | [COVID-Net-Small] |
| - | - | - | [COVID-Net-Large] |
| 76 | - | - | Mobilenet V2 |
| 76 | - | - | [ResNeXt50-32x4d] |
| 73 | - | - | [ResNet-18] |
| 81 | - | - | [Densenet-169] |
Confusion Matrix on both datasets coming soon !!
The COVID-CT-Dataset has 288 CT images containing clinical findings of COVID-19. We are continuously adding more COVID CTs.
The images are collected from medRxiv and bioRxiv papers about COVID-19. CTs containing COVID-19 abnormalities are selected by reading the figure captions in the papers. All copyrights of the data belong to medRxiv and bioRxiv.
Please refer to the preprint for details: COVID-CT-Dataset: A CT Scan Dataset about COVID-19
The current COVIDx dataset is constructed by the following open source chest radiography datasets:
- https://github.com/ieee8023/covid-chestxray-dataset
- https://www.kaggle.com/c/rsna-pneumonia-detection-challenge
We especially thank the Radiological Society of North America and others involved in the RSNA Pneumonia Detection Challenge, and Dr. Joseph Paul Cohen and the team at MILA involved in the COVID-19 image data collection project, for making data available to the global community.
Download the datasets listed above
git clone https://github.com/ieee8023/covid-chestxray-dataset.git- go to this link to download the RSNA pneumonia dataset
- Create a
datadirectory and within the data directory, create atrainandtestdirectory - Use COVIDNet.ipynb to combine the two dataset to create COVIDx. Make sure to remember to change the file paths.
- We provide the train and test txt files with patientId, image path and label (normal, pneumonia or COVID-19). The description for each file is explained below:
- train_COVIDx.txt: This file contains the samples used for training.
- test_COVIDx.txt: This file contains the samples used for testing.
Chest radiography images distribution
| Type | Normal | Pneumonia | COVID-19 | Total |
|---|---|---|---|---|
| train | 7966 | 8514 | 66 | 16546 |
| test | 100 | 100 | 10 | 210 |
Check out this repository for more medical applications with deep-learning in PyTorch https://github.com/black0017/MedicalZooPytorch from https://github.com/black0017