Pneumonia Detection

By: Ali A., Ayush A., Sofia G., Sofia S., Paola

Description

What is pneumonia?

Pneumonia is mostly spread when people cough, sneeze or talk, sending respiratory droplets into the air. These droplets can then be inhaled by close contacts. Less often, you can get pneumonia from touching an object or surface that has the germ on it and then touching your nose or mouth

Overall goal

What are we trying to do?

Have an automated pneumonia detector

• many people can be trained but only few who are specialized who can see the scans to detect them
• The AI system can detect the super easy pneumonia symptoms

25,265,354,215

Total diagnosed pneumonias

Discussion

Wash your hands

Why do you think this is important?

Bacteria and immune system

Do you think the bacteria are the ones that cause pneumonia?

Recover

How long does it take someone to recover from this experience?

Guess!

Pneumonia

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Pneumonia is an infection of one or both of the lungs caused by bacteria, viruses, or fungi. It is a serious infection in which the air sacs fill with pus and other liquid.

Data Discussion

Binary Classification

• Call the two classes { 0, 1 }

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• Output of model is predicted probability that input belongs to class 1

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Rule/boundary

Logistic Regression

• Input to logistic regression is flattened 64 x 64 x 3 image = vector of length 12,288
• Linear means different things for different dimensions - 2D - line, 3D - plane, 12288D - hyperplane
• Probabilities assigned based on distance from this linear decision boundary

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Neural networks

• Maps a vector input to a vector output → 64x64x3 image to a probability of pneumonia.
• Learn highly nonlinear decision boundaries

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Convolutional neural networks

• Type of neural network that takes advantage of the 2D structure of images to share weights between neurons and reduce number of parameters that need to be learned.
• Takes advantage of the fact that if we take an image of a cat and slide the cat around, it’s still an image of a cat.

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Transfer Learning

We used large models such as VGG16 and ResNet50 that were pre-trained to classify images and fine-tuned them to classify whether images of x-rays were healthy or have pneumonia

This allowed the models to easily classify the images from their pre-trained data and specialize to downstream tasks

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Ethics

• Could produce false positives / false negatives
• Misdiagnosis as well as unnecessary treatments
• AI might need private data from patients
• Unapproved data could result to consequences
• Ai could replace professionals in their jobs

• Can provide professionals with early detection
• Lead to faster treatment solutions
• Can reduce the workload on healthcare professionals
• Faster results for patient
• AI models can be scaled to analyze large number of cases in a small amount of time

Conclusion

The importance of having an automated pneumonia detector has become increasingly demanded. While many people can be trained to interpret scans there is a large error gap.

Due to AI systems we have the ability to detect subtle signs of pneumonia enabling an earlier diagnosis. Advanced models for classification such, as regression and multi layer neural networks have been developed to effectively analyze medical images and identify pneumonia cases

These models utilize the aspects of images, and therefore be fine tuned to differentiate between healthy and infected x ray scans, showing the potential for ai to change the medical field.

Thank you!

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