Pneumonia is a form of acute respiratory infection that affects the lungs. The lungs are made up of small sacs called alveoli, which fill with air when a healthy person breathes. When an individual has pneumonia, the alveoli are filled with pus and fluid, which makes breathing painful and limits oxygen intake.
The aim was to take the dataset which contains 5,863 X-Ray images, divided into normal and pneumonia, and to train a traditional (classification) machine learning algorithm to be able to detect the pneumonia cases. This is a challenge as this task would usually be done using a CNN model, instead of a machine learning algorithm.
The model (KNN) was evaluated on its accuracy which reached 84.45% accuracy on the test set. below is the model confusion matrix:
Below is 16 images pulled randomly from the data test set, each image is labeled with the its true finding as well as the model prediction:
