Detecting Covid-19 with Chest X-ray

COVID-19 pandemic is one of the biggest challenges for the healthcare system right now. It is a respiratory disease that affects our lungs and can cause lasting damage to the lungs that led to symptoms such as difficulty in breathing and in some cases pneumonia and respiratory failure. In this article, we will use X-ray data of lungs normal and COVID-positive patients and train a model to differentiate between them.

Dataset and Models Used:

The dataset used in this post is the winner of the Kaggle community award. The dataset is collected by researchers from Qatar and Bangladesh. This dataset contains 3 types of images:

• COVID-19 positive (219 images)
• Viral Pneumonia (1341 images)
• Normal X-ray (1345 images)

Therefore, we have to classify among these 3 different classes and we will use the softmax layer for classification.

These images have the size (1024, 1024) and 3 color channels. The authors of the dataset also trained a ResNet -34 model and achieved an accuracy of  98.5%.

Implementation:

• In this article, we will use the Xception model with the help of Keras API. This model obtained an ImageNet top-1 accuracy of 79% and a top-5 accuracy of 95%.
• First, we need to import the necessary modules.

• Now, we will use Kaggle API to download the dataset to the system. First, we will require the API key, to do this just navigate to the profile section on Kaggle and download a JSON file containing our details for API, after that just upload this to colab or locate in the local jupyter environment.

• Now, we unzip the dataset into the desired folder.

• Now we preprocessed the dataset, we reduce the image size from (1024, 1024) to (299,299) [the maximum size accepted by the Xception model], and we divide it into a batch size of 16.

Downloading data from 
https://storage.googleapis.com/tensorflow/keras-applications/xception/
xception_weights_tf_dim_ordering_tf_kernels_notop.h5
83689472/83683744 [==============================] - 1s 0us/step
Model: "xception"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to 
==================================================================================================
input_1 (InputLayer) [(None, 299, 299, 3) 0 
__________________________________________________________________________________________________
block1_conv1 (Conv2D) (None, 149, 149, 32) 864 input_1[0][0] 
__________________________________________________________________________________________________
block1_conv1_bn (BatchNormaliza (None, 149, 149, 32) 128 block1_conv1[0][0] 
__________________________________________________________________________________________________
block1_conv1_act (Activation) (None, 149, 149, 32) 0 block1_conv1_bn[0][0] 
__________________________________________________________________________________________________
block1_conv2 (Conv2D) (None, 147, 147, 64) 18432 block1_conv1_act[0][0] 
__________________________________________________________________________________________________
block1_conv2_bn (BatchNormaliza (None, 147, 147, 64) 256 block1_conv2[0][0] 
__________________________________________________________________________________________________
block1_conv2_act (Activation) (None, 147, 147, 64) 0 block1_conv2_bn[0][0] 
__________________________________________________________________________________________________
block2_sepconv1 (SeparableConv2 (None, 147, 147, 128 8768 block1_conv2_act[0][0] 
__________________________________________________________________________________________________
block2_sepconv1_bn (BatchNormal (None, 147, 147, 128 512 block2_sepconv1[0][0] 
__________________________________________________________________________________________________
block2_sepconv2_act (Activation (None, 147, 147, 128 0 block2_sepconv1_bn[0][0] 
__________________________________________________________________________________________________
block2_sepconv2 (SeparableConv2 (None, 147, 147, 128 17536 block2_sepconv2_act[0][0] 
__________________________________________________________________________________________________
block2_sepconv2_bn (BatchNormal (None, 147, 147, 128 512 block2_sepconv2[0][0] 
__________________________________________________________________________________________________
conv2d (Conv2D) (None, 74, 74, 128) 8192 block1_conv2_act[0][0] 
__________________________________________________________________________________________________
block2_pool (MaxPooling2D) (None, 74, 74, 128) 0 block2_sepconv2_bn[0][0] 
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 74, 74, 128) 512 conv2d[0][0] 
__________________________________________________________________________________________________
add (Add) (None, 74, 74, 128) 0 block2_pool[0][0] 
 batch_normalization[0][0] 
__________________________________________________________________________________________________
block3_sepconv1_act (Activation (None, 74, 74, 128) 0 add[0][0] 
__________________________________________________________________________________________________
block3_sepconv1 (SeparableConv2 (None, 74, 74, 256) 33920 block3_sepconv1_act[0][0] 
__________________________________________________________________________________________________
block3_sepconv1_bn (BatchNormal (None, 74, 74, 256) 1024 block3_sepconv1[0][0] 
__________________________________________________________________________________________________
block3_sepconv2_act (Activation (None, 74, 74, 256) 0 block3_sepconv1_bn[0][0] 
__________________________________________________________________________________________________
block3_sepconv2 (SeparableConv2 (None, 74, 74, 256) 67840 block3_sepconv2_act[0][0] 
__________________________________________________________________________________________________
block3_sepconv2_bn (BatchNormal (None, 74, 74, 256) 1024 block3_sepconv2[0][0] 
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 37, 37, 256) 32768 add[0][0] 
__________________________________________________________________________________________________
block3_pool (MaxPooling2D) (None, 37, 37, 256) 0 block3_sepconv2_bn[0][0] 
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 37, 37, 256) 1024 conv2d_1[0][0] 
__________________________________________________________________________________________________
.............

(Trimmed model Summary)

==================================================================================================
Total params: 20,861,480
Trainable params: 0
Non-trainable params: 20,861,480
________________________________________________________________________

• Now, we apply some data augmentation on the dataset and prepare it for training. After that, we plot some training images. We will split the dataset in such a way that we have 75% data for training and 25% for the test/validation.

Now, we define our model, First, we will import our base model i.e Xception (we use imagenet pretrained weights) in our sequential model, flatten the top layer and apply a dense layer (fully connected layer) and a softmax classification layer to classify among 3 classes. To prevent the model from overfitting we will also add some dropout layers.

Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param # 
=================================================================
xception (Functional) (None, 10, 10, 2048) 20861480 
_________________________________________________________________
dropout (Dropout) (None, 10, 10, 2048) 0 
_________________________________________________________________
flatten (Flatten) (None, 204800) 0 
_________________________________________________________________
dropout_1 (Dropout) (None, 204800) 0 
_________________________________________________________________
dense (Dense) (None, 16) 3276816 
_________________________________________________________________
dense_1 (Dense) (None, 3) 51 
=================================================================
Total params: 24,138,347
Trainable params: 3,276,867
Non-trainable params: 20,861,480
_________________________________________________________________

Now we will compile and train the model, we use Adam Optimizer with a 0.001 learning rate. We will train the model for 30 epochs.

Epoch 1/30
137/137 [==============================] - 121s 886ms/step - 
loss: 5.7757 - accuracy: 0.8528 - val_loss: 3.4022 - val_accuracy: 0.8966
Epoch 2/30
137/137 [==============================] - 119s 867ms/step - 
loss: 3.3137 - accuracy: 0.9028 - val_loss: 2.0748 - val_accuracy: 0.9228
Epoch 3/30
137/137 [==============================] - 119s 866ms/step - 
loss: 2.2811 - accuracy: 0.9161 - val_loss: 2.2661 - val_accuracy: 0.9186
Epoch 4/30
137/137 [==============================] - 119s 867ms/step - 
loss: 1.6122 - accuracy: 0.9339 - val_loss: 3.8654 - val_accuracy: 0.8648
Epoch 5/30
137/137 [==============================] - 120s 877ms/step - 
loss: 1.0704 - accuracy: 0.9440 - val_loss: 1.6559 - val_accuracy: 0.9214
Epoch 6/30
137/137 [==============================] - 120s 875ms/step - 
loss: 0.7675 - accuracy: 0.9509 - val_loss: 1.3920 - val_accuracy: 0.9255
Epoch 7/30
137/137 [==============================] - 120s 872ms/step - 
loss: 0.5744 - accuracy: 0.9509 - val_loss: 1.2669 - val_accuracy: 0.9021
Epoch 8/30
137/137 [==============================] - 119s 872ms/step - 
loss: 0.4065 - accuracy: 0.9528 - val_loss: 1.1800 - val_accuracy: 0.9145
Epoch 9/30
137/137 [==============================] - 118s 864ms/step - 
loss: 0.2160 - accuracy: 0.9638 - val_loss: 0.7624 - val_accuracy: 0.9379
Epoch 10/30
137/137 [==============================] - 119s 865ms/step - 
loss: 0.2552 - accuracy: 0.9606 - val_loss: 0.4897 - val_accuracy: 0.9421
Epoch 11/30
137/137 [==============================] - 118s 864ms/step - 
loss: 0.2015 - accuracy: 0.9651 - val_loss: 0.4510 - val_accuracy: 0.9476
Epoch 12/30
137/137 [==============================] - 121s 880ms/step - 
loss: 0.1473 - accuracy: 0.9725 - val_loss: 0.3458 - val_accuracy: 0.9352
Epoch 13/30
137/137 [==============================] - 121s 880ms/step - 
loss: 0.1534 - accuracy: 0.9656 - val_loss: 0.5945 - val_accuracy: 0.9297
Epoch 14/30
137/137 [==============================] - 120s 876ms/step - 
loss: 0.1315 - accuracy: 0.9734 - val_loss: 0.4655 - val_accuracy: 0.9407
Epoch 15/30
137/137 [==============================] - 121s 882ms/step - 
loss: 0.1127 - accuracy: 0.9661 - val_loss: 0.3728 - val_accuracy: 0.9186
Epoch 16/30
137/137 [==============================] - 121s 882ms/step - 
loss: 0.1198 - accuracy: 0.9716 - val_loss: 0.4312 - val_accuracy: 0.9476
Epoch 17/30
137/137 [==============================] - 120s 875ms/step - 
loss: 0.1046 - accuracy: 0.9771 - val_loss: 0.4035 - val_accuracy: 0.9393
Epoch 18/30
137/137 [==============================] - 119s 870ms/step - 
loss: 0.0872 - accuracy: 0.9761 - val_loss: 0.8248 - val_accuracy: 0.9145
Epoch 19/30
137/137 [==============================] - 120s 874ms/step - 
loss: 0.1116 - accuracy: 0.9752 - val_loss: 0.3309 - val_accuracy: 0.9393
Epoch 20/30
137/137 [==============================] - 120s 877ms/step - 
loss: 0.1261 - accuracy: 0.9729 - val_loss: 0.5384 - val_accuracy: 0.8924
Epoch 21/30
137/137 [==============================] - 119s 869ms/step - 
loss: 0.0840 - accuracy: 0.9748 - val_loss: 0.5690 - val_accuracy: 0.9366
Epoch 22/30
137/137 [==============================] - 119s 868ms/step - 
loss: 0.0942 - accuracy: 0.9761 - val_loss: 0.3517 - val_accuracy: 0.9448
Epoch 23/30
137/137 [==============================] - 120s 876ms/step - 
loss: 0.1207 - accuracy: 0.9656 - val_loss: 0.2871 - val_accuracy: 0.9434
Epoch 24/30
137/137 [==============================] - 118s 864ms/step - 
loss: 0.0959 - accuracy: 0.9729 - val_loss: 0.4589 - val_accuracy: 0.9366
Epoch 25/30
137/137 [==============================] - 119s 867ms/step - 
loss: 0.0945 - accuracy: 0.9748 - val_loss: 0.3964 - val_accuracy: 0.9490
Epoch 26/30
137/137 [==============================] - 119s 871ms/step - 
loss: 0.1039 - accuracy: 0.9761 - val_loss: 0.3048 - val_accuracy: 0.9393
Epoch 27/30
137/137 [==============================] - 119s 866ms/step - 
loss: 0.0905 - accuracy: 0.9739 - val_loss: 0.3308 - val_accuracy: 0.9407
Epoch 28/30
137/137 [==============================] - 120s 873ms/step - 
loss: 0.0757 - accuracy: 0.9766 - val_loss: 0.1871 - val_accuracy: 0.9517
Epoch 29/30
137/137 [==============================] - 119s 871ms/step - 
loss: 0.1012 - accuracy: 0.9688 - val_loss: 0.7361 - val_accuracy: 0.9297
Epoch 30/30
137/137 [==============================] - 120s 874ms/step - 
loss: 0.0713 - accuracy: 0.9780 - val_loss: 0.3497 - val_accuracy: 0.9434

Results and Conclusion: We have obtained an accuracy on 97.8% on training set and 94.3% on validation set in  just 30 epochs on Xception model, which is close to the 98.3% accuracy as reported by the authors of paper.