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Building Advanced Deep Learning and NLP Projects

Project Creation: Part Two

Explore how to process X-ray images using data augmentation techniques and train a convolutional neural network (CNN) for COVID-19 detection. This lesson guides you through loading the dataset, applying rescaling, shear, zoom, and horizontal flip transformations, and training the model with TensorFlow's updated methods. By completing this project, you'll understand practical steps to build an image-based disease detection system and achieve about 96% accuracy.

We'll cover the following...

We successfully built our model architecture in the previous lesson. However, we still need to process our images before actually feeding them to the network.

Processing the images

So, let’s process our training and validation images, which you downloaded from the link provided in the previous lesson.

Python 3.5
train_datagen = image.ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
test_datagen = image.ImageDataGenerator(rescale = 1./255)
print('Created the Data Generator Objects.')

Explanation:

  • We used ImageDataGenerator, which will generate batches of tensor image data with real-time data augmentation. The data will be looped over (in batches). This will help prevent over-fitting as we have a
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