  Diabetic retinopathy (DR) results in vision loss if not treated early. A
computer-aided diagnosis (CAD) system based on retinal fundus images is an
efficient and effective method for early DR diagnosis and assisting experts. A
computer-aided diagnosis (CAD) system involves various stages like detection,
segmentation and classification of lesions in fundus images. Many traditional
machine-learning (ML) techniques based on hand-engineered features have been
introduced. The recent emergence of deep learning (DL) and its decisive victory
over traditional ML methods for various applications motivated the researchers
to employ it for DR diagnosis, and many deep-learning-based methods have been
introduced. In this paper, we review these methods, highlighting their pros and
cons. In addition, we point out the challenges to be addressed in designing and
learning about efficient, effective and robust deep-learning algorithms for
various problems in DR diagnosis and draw attention to directions for future
research.
