  Cataracts are the leading cause of visual impairment and blindness globally.
Over the years, researchers have achieved significant progress in developing
state-of-the-art machine learning techniques for automatic cataract
classification and grading, aiming to prevent cataracts early and improve
clinicians' diagnosis efficiency. This survey provides a comprehensive survey
of recent advances in machine learning techniques for cataract
classification/grading based on ophthalmic images. We summarize existing
literature from two research directions: conventional machine learning methods
and deep learning methods. This survey also provides insights into existing
works of both merits and limitations. In addition, we discuss several
challenges of automatic cataract classification/grading based on machine
learning techniques and present possible solutions to these challenges for
future research.
