"# covid-19-detector"
Do NOT use any result of this repo for scientific purposes without medical assistance
We assume you have an GPU available and python 3.6 installed. Make sure you have installed all the correct drivers for gpu training. Check out https://www.tensorflow.org/install/gpu
for information about GPU setup.
Assuming you're ok, run pip install -r requirements.txt
to install all necessary packages.
Set the following options to train:
-p
: Path to the dataset (dataset/)-g
: If use or not gpu in case it is possible. (Default = True)--network
: Base network to use. Supports vgg16, vgg19, resnet50, resnet152, efficientnet-b0, efficientnet-b1,..., efficientnet-b7 (Default = resnet50)--hf
: Augment with horizontal flips in training. (Default=True)--vf
: Augment with vertical flips in training. (Default=True)--rot
: Augment with 90 degree rotations in training. (Default=True)--num_epochs
: Number of epochs. (Default = 100)--config_filename
: Name of txt file that stores all the metadata related to the training (to be used when testing)--input_weight_path
: Input path for weights for classifier model--mn
: Name of the model
Example of training command line:
python train.py -p dataset/ -g True --network vgg19 --mn vgg19_1
Set the following options to predict:
-w
: Path to the weights file (hdf5 or h5)-c
: Path to the config file-p
: Path to the folder containing images to be classified-g
: Use GPU or not (Default = True)
Set the following options to run the vis script:
-p
: Path to the image-w
: Path to the weights file (hdf5 or h5)-c
: Path to the config file-g
: Use GPU or not (Default = True)
To run the server just enter the following line on cmd:
python server.py -m path/to/model/weights/file
The weights file were generated after training
After, to make an http request run request.py:
python request.py -p path/to/folder/containing/images/to/be/classified
All the models were run on a GPU RTX 2080 6GB. Each epoch took ~22s
- VGG19: ~96% accuracy and ~90% val accuracy after 29 epochs
- Many thank to Società Italiana di Radiologia Medica e Interventistica for providing many images
- Many thanks to https://www.medicalimages.com for providing some CT images of normal lungs
- Many thanks to Joseph Paul Cohen who makes part of the dataset available
- Many thanks to Adrian Rosebrock for providing some interesting code