| Platform | Build Status |
|---|---|
| AWS EC2 | |
| oneAPI DevCloud |
Coronavirus (2019-nCoV infection) Recognition using Deep Neural Networks for Computer Tomography (CT) & X-Ray image analysis.
On Dec. 31, 2019, the World Health Organization (WHO) learned of several cases of a respiratory illness clinically resembling viral pneumonia and manifesting as fever, cough, and shortness of breath. The newly discovered virus emerging from Wuhan City, Hubei Province of China, was temporarily named “novel coronavirus” (2019-nCoV). It is now known officially as COVID-19. This new coronavirus belongs to a family of viruses that include Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS). The outbreak is escalating quickly, with hundreds of thousands of confirmed COVID-19 cases reported globally. Early disease recognition is critical not only for prompt treatment, but also for patient isolation and effective public health containment and response. Thus we propose the use of AI based CT image analysis for recognition of coronavirus infection under Project Treatise of Medical Image Processing v0.2.0.. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. Thus we propose the use of Deep Neural Networks, as an initial experiment we used ChexNeXt Pneumonia Detection Model as the baseline architecture where we use transfer learning to detect pneumonia. Secondly we use three different convolutional neural network architectures (ResNet50, InceptionV3 and Inception-ResNetV2) for the detection of coronavirus pneumonia infected patients using chest X-ray radiographs.
- Get a Microsoft Azure Account
- Create your Data Science Virtual Machine for Linux
- Clone TMIP Repo
git clone https://github.com/TebogoNakampe/TMIP-2019-nCoV-Recognition.git cd TMIP-2019-nCoV-Recognition/TMIP_Azure/ pip install -r requirements.txt - Get Data and set Path to "rsna-data" and "covid-chestxray-dataset" in configuration file
cd .. bash tmip_data.sh find "$(cd ..; pwd)" -name "rsna-data" #Copy the output to the config.yml find "$(cd ..; pwd)" -name "covid-chestxray-dataset" #Copy the output to the config.yml
- Preprocess Data
cd TMIP_Azure/ bash tmip_preprocess.sh - Train ML Model
bash tmip_train.sh
- Request access to the oneAPI DevCloud
- Clone TMIP Repo
git clone https://github.com/TebogoNakampe/TMIP-2019-nCoV-Recognition.git cd TMIP-2019-nCoV-Recognition/TMIP_oneAPI/ pip install -r requirements.txt - Get Data and set Path to "rsna-data" and "covid-chestxray-dataset" in configuration file
cd .. bash tmip_data.sh find "$(cd ..; pwd)" -name "rsna-data" #Copy the output to the config.yml find "$(cd ..; pwd)" -name "covid-chestxray-dataset" #Copy the output to the config.yml
- Preprocess Data
cd TMIP_Azure/ bash tmip_preprocess.sh - Train ML Model
qsub -I -l walltime=24:00:00 qsub -l nodes=4:gpu:ppn=2 -l walltime=24:00:00 -d . tmip.sh
If you find this useful, please cite our work as follows:
@article{tebogonakampe2020TMIP, author = {Tebogo Nakampe, Thabo Koee, title = {Treatise of Medical Image Processing v020}, journal = {TMIPv020}, year = {2020}, }
Please contact "info@4ir-abi.co.za" if you have any questions.