基于CNN的2019-nCov新冠肺炎检测鉴定云平台python源码+项目说明.zip

# 基于CNN的2019-nCov新冠肺炎检测鉴定云平台 - 使用DenseNet网络进行迁移学习，构建新冠肺炎CT影像检测模型。 - 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。 - 基于 [keras-flask-deploy-webapp](https://github.com/mtobeiyf/keras-flask-deploy-webapp) 的Flask框架部署网络检测平台。  ------------------ ## Deploy Pytorch Model with Flask as Web App ```shell # 1. First, clone the repo $ git clone [email protected]: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.py ``` Open http://localhost:5000 and have fun. :smiley: ------------------ ## Run with Docker With **[Docker](https://www.docker.com)**, you can quickly build and run the entire application in minutes :whale: ```shell # 1. First, clone the repo $ git clone [email protected]: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_app ``` Open http://localhost:5000 and wait till the webpage is loaded. ------------------ ## Dataset - [COVIDx CT](https://www.kaggle.com/hgunraj/covidxct)   ## Model The model training file is [COVID-19.ipynb](./COVID-19.ipynb). ## Performance    ## More resources [Building a simple Keras + deep learning REST API](https://blog.keras.io/building-a-simple-keras-deep-learning-rest-api.html)