Cataract disease is the world's leading cause of blindness with a majority of the world's population affected by it; however, it is easily treatable with surgery.

In Mexico, of the 16 million people who suffer from some form of vision loss, 63% of the population (~10 million people) are blind due to cataract disease.

There are four obstacles to receiving cataract surgery:

  • Lack of awareness

  • Bad service

  • Cost

  • Distance for treatment


63% of the Mexican population has a smartphone, so a smartphone application would be the best mode to reach our audience.

Our model trains on cropped images of the pupils and irises of cataract patients in the Cataract Mobile Periocular Database (CMPD). This database contains images of pre-surgery and post-surgery cataract patients taken from a mobile device with 16-megapixel camera resolution.


The eye, the focal point of our diagnosis, must be extracted from these images. We utilize Python's mlxtend.image package to identify eye landmarks and select the top and bottom two landmarks to create a bounding box for the cropped image.

We are only interested in the area of the iris and pupil and disregard features irrelevant for our classification model.