Covid-19 Diagnosis using Radiography Images

Classifying radiography images using a pre-trained ResNet-18 architecture

In this notebook, we will try to classify images from the Covid-19 Radiography Dataset[1] using a pre-trained ResNet-18 network. We will be using the PyTorch library for building our network.

Disclaimer: This model should be used only for learning purposes as Covid-19 diagnosis is an ongoing research topic.

Download the dataset

Firstly, you need to download the dataset from Kaggle. Check these steps for detailed instructions.

Data preprocessing

As a first, step download the dataset from Kaggle and create a new PyTorch dataset using the ImageFolder method.

Also, we are defining a transformer to Resize the images to 224x224 px and then converting the image to a Tensor.

Next, we. split the dataset into training, validation and testing sets. We would be using 20% of the data for testing and 10% of the data for validation using a SubsetRandomSampler.

Next, lets define the DataLoader for training, testing and validation sets.

Now, that our DataLoader is defined, lets use the train_loader to visualize a few images along with their classes.


Defining the model

Next, lets define the model. We will be using a pre-trained Resnet18 architecture for our classification task. As the number of images in our dataset is relatively less, using a transfer learning will be helpful.

Downloading: "" to /root/.cache/torch/checkpoints/resnet18-5c106cde.pth

HBox(children=(FloatProgress(value=0.0, max=46827520.0), HTML(value='')))


Let’s add fully connected layers at the end of the network, to adjust the final layer’s output to correspond to the number of classes in our dataset.

We would be using CrossEntropyLoss as the loss function and a learning rate of 3e-5 for training.


Finally, lets train the network for 20 epochs

Epoch: 1 	Training Loss: 0.569099 	Validation Loss: 0.045166
Validation loss decreased (inf --> 0.045166).  Saving model ...
Epoch: 2 	Training Loss: 0.447157 	Validation Loss: 0.035986
Epoch: 19 	Training Loss: 0.229328 	Validation Loss: 0.016049
Epoch: 20 	Training Loss: 0.248973 	Validation Loss: 0.013585
Validation loss decreased (0.014983 --> 0.013585).  Saving model ...


Now, that the training is complete, lets evaluate the performance of the network by predicting the classes for test dataset.

Test Loss: 0.037036

Test Accuracy of COVID-19: 100% (48/48)
Test Accuracy of NORMAL: 97% (268/276)
Test Accuracy of Viral Pneumonia: 91% (233/256)

Test Accuracy (Overall): 94% (549/580)


We used a pre-trained ResNet18 architecture for classifying images from the Radiography dataset. Our model gives a good accuracy of around 94% without reinventing the wheel. This demonstrates the power of transfer learning and shows how new problems can be tackled using existing models pre-trained on much larger datasets. With some minor enhancements like data augmentation, the accuracy can further be improved.


[1] M.E.H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M.A. Kadir, Z.B. Mahbub, K.R. Islam, M.S. Khan, A. Iqbal, N. Al-Emadi, M.B.I. Reaz, “Can AI help in screening Viral and COVID-19 pneumonia?” arXiv preprint, 29 March 2020,

Vivek Maskara
Vivek Maskara
GRA at The Luminosity Lab, ASU | Ex Senior Software Engineer, Zeta | Volunteer, Wikimedia Foundation

Seeking Summer 2021 internships. Check out my Resume.