4. Introduction
Inside of today’s presentation, you will see what motivates us to
work [1]:
• Sample an open source dataset of X-ray images for patients
who have tested positive for COVID-19 or not.
• Train a CNN to automatically detect COVID-19 in X-ray images via
the dataset we created.
• Collection of ”weapons”: Discord, Trello, GitHub, Google Colab,
TensorFlow, Keras, Sckit-learn, Matplotlib, Python.. and more.
• Evaluate the team results from an educational perspective.
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7. Cross-validation
What’s?
It’s a approach to validate the generalization capability of the model.
How it works?
Figure 2: Cross-Validation definition. URL: https://didatica.tech/
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10. Data Augmentation
What’s?
Data augmentation encompasses a wide range of techniques used to
generate ”new” training samples from the original sample.
• Translations
• Rotations
• Changes in scale
• Horizontal (and in some cases, vertical) flips
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11. Data Augmentation
How it works?
train_datagen = ImageDataGenerator(rotation_range = 20,
zoom_range = 0.2)
data_aug = train_datagen.flow(trainX, trainY, batch_size=BS)
Figure 4: Data augmentation COVID-19 results: initial tests
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13. TensorBoard
What’s?
Is a tool that allows the visualization of neural network statistics
using a monitor. For example, training parameters: loss, accuracy and
weights.
The hard problem...
• Keras train consuming high RAM memory (Colab limit 12GB).
• K-fold cross-validation: when the dataset is randomly split up
into ’k’ groups.
Finding possible solutions...
• Modify callbacks arguments on TensorBoard.
• Clear memory inside the fit() method (using callbacks).
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16. Checkpoint
What’s?
To save the model for each epoch, we can train our model without
worrying about problems that may happen, such as internet crashes,
machine crashes, etc.
How it works?
It works using a callback function in the fit() method for train models.
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19. Future works
Limitations, improvements, and future work:
• One of the biggest limitations of the method discussed in this
presentation is high usage of RAM.
• Furthermore, we need to be concerned with what the model is
actually ”learning”.
• And finally, future (and better) COVID-19 detectors will be
multi-modal.
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20. References i
A. Rosebrock.
Detecting covid-19 in x-ray images with keras, tensorflow, and
deep learning.
URL: https://www. pyimagesearch.
com/2020/03/16/detecting-covid-19-in-x-rayimages-with-keras-
tensorflow-and-deep-learning,
2020.
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