Pneumonia Prediction Using chest x-ray Image Machine Learning

Chest x–ray: An x–ray exam will allow your doctor to see your lungs, heart and blood vessels to help determine if you have pneumonia. When interpreting the x–ray, the radiologist will look for white spots in the lungs (called infiltrates) that identify an infection. Build an algorithm to automatically identify whether a patient is suffering from pneumonia or not by looking at chest X-ray images. The algorithm had to be extremely accurate because lives of people is at stake. This is a Flask web app designed to analyze a chest x-ray and predict whether a person has TB/pneumonia or not.

Models : 

The model is based on a  convolutional neural network that has been trained on a dataset of 800 images from two sources

The model has an overall accuracy of 83% and an F1 score of 80%.

A negative prediction means that the chest X-ray is most likely normal while the contrary is implied by a positive prediction

Environment and tools

1. scikit-learn
2. keras
3. numpy
4. pandas
5. matplotlib
6. flask
7. tensorflow

Datasets Link

The dataset can be downloaded from the kaggle website which can be found here.

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