基于CNN的2019-nCov新冠肺炎检测鉴定云平台
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使用DenseNet网络进行迁移学习，构建新冠肺炎CT影像检测模型。
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使用GradCam++算法对类激活图进行可视化，了解神经网络决策依据。
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基于 keras-flask-deploy-webapp 的Flask框架部署网络检测平台。
Deploy Pytorch Model with Flask as Web App
# 1. First, clone the repo
$ git clone git@github.com:lxysl/covid_web.git
$ cd covid_web
# 2. Install Python packages
$ pip install -r requirements.txt
# please note the torchcam package needs to be installed separately
# for more details, see https://github.com/frgfm/torch-cam/issues/72#issuecomment-943168322
$ pip install git+https://github.com/frgfm/torch-cam.git#egg=torchcam
# 3. Run!
$ python app.pyOpen http://localhost:5000 and have fun. 😃
Run with Docker
With Docker, you can quickly build and run the entire application in minutes 🐳
# 1. First, clone the repo
$ git clone git@github.com:lxysl/covid_web.git
$ cd covid_web
# 2. Build Docker image
$ docker build -t covid_web .
# 3. Run!
$ docker run -it --rm -p 5000:5000 keras_flask_appOpen http://localhost:5000 and wait till the webpage is loaded.
Dataset - COVIDx CT
Model
The model training file is COVID-19.ipynb.
Performance
