1. The team trained a CNN model on a COVID-19 X-ray image dataset to automatically detect COVID-19 in chest X-rays. They used tools like TensorFlow, Keras, and Python. 2. They evaluated the model using techniques like cross-validation, data augmentation, TensorBoard for visualization, and checkpointing to save models during training. 3. Future work could focus on reducing memory usage, improving model interpretation, and developing multi-modal COVID detectors using different types of medical data.

1. 2ª Mini-conf PredictCovid
Field: Artificial Intelligence
Team results
June 19, 2020
Alex C., Andrelise N., Rodrigo S., Rodrigo T., Arthur T.

2. Summary
1. Introduction
2. Cross-validation
3. Data Augmentation
4. TensorBoard
5. Checkpoint
6. Future works
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3. Introduction

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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5. Introduction
Figure 1: Robust toolset and source-code availability.
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6. Cross-validation

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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8. Cross-validation
Figure 3: Cross-validation COVID-19 results: initial tests. 5

9. Data Augmentation

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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12. TensorBoard

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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14. TensorBoard
Figure 5: TensorBoard COVID-19 results: initial tests
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15. Checkpoint

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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17. Checkpoint
Figure 6: Checkpoint COVID-19 results: initial implementation
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18. Future works

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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