Malaria image recognition

This project looks into Machine Learning approaches to detect Malaria in red blood cells.

Dependencies

For the image processing we use opencv, which can be installed from here: https://docs.opencv.org/4.2.0/df/d65/tutorial_table_of_content_introduction.html. After installing opencv it is recommended one sets up a virtual python3.6+ environment and installs the dependencies using pip3 install -r path/to/requirements.txt.

Dataset

The dataset can be found here: https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria/data

When unpacking the dataset, only unpack the nested cell_images/ directory. This directory contains two sets of data. One directory (Uninfected/) contains images of normal cells, the other (Parasitized/) infected cell images. So the dataset should have the following folder structure:

- cell_images
  - Parasitized
  - Uninfected

Configuring the project

After making sure the data is available as specified above, users need to specify some global variables in the config.py file. The file should contain at least the dataset's location on the machine as well as the size the images will be scaled to.

After this is done, be sure to run the data_wrappers.py script before running any of the classifier scripts. This will create .npy files in the project folder which hold the preprocessed data.

Running the classifiers

The classifier scripts can be ran normally using python3 name_of_script.py.