Identified COVID-19 in X-ray images with deep learning.

 Project structure :

 

 

 

Our coronavirus (COVID-19) chest X-ray data is in the dataset/ directory where our two classes of data are separated into covid/ and normal/

 

I have created train_covid19.py file to train the model.

 

Three command line arguments (parameters) required to run this file :

 

--dataset: The path to our input dataset of chest X-ray images.

--plot: An optional path to an output training history plot. By default the plot is named plot.png unless otherwise specified via the command line.

--model: The optional path to our output COVID-19 model; by default it will be named covid19.model.

 

 

To load our data, we grab all paths to images in in the --dataset directory. Then, for each imagePath, we:

 

·         Extract the class label (either covid or normal) from the path.

·         Load the image, and preprocess it by converting to RGB channel ordering, and resizing it to 224×224 pixels so that it is ready for our Convolutional Neural Network .

·         Update our data and labels lists respectively.

 

After that  we will encode our labels and create our training/testing splits:

 

 

 

We’re now ready to compile and train our COVID-19 deep learning model

 

 

For evaluation, we first make predictions on the testing set and grab the prediction indices.

 

 

Next we’ll compute a confusion matrix for further statistical evaluation

 

 

We then plot our training accuracy/loss history for inspection, outputting the plot to an image file

 

 

Finally we save our COVID-19 classifier model to disk

 

 

 

For testing this model.

 

I have created one class in file test_covid19.py using tf.keras  model load method and image load. This way model will predict and identified the images if infected to covid or normal.