  Diabetic Retinopathy (DR) is a severe complication of diabetes that can cause
blindness. Although effective treatments exist (notably laser) to slow the
progression of the disease and prevent blindness, the best treatment remains
prevention through regular check-ups (at least once a year) with an
ophthalmologist. Optical Coherence Tomography Angiography (OCTA) allows for the
visualization of the retinal vascularization, and the choroid at the
microvascular level in great detail. This allows doctors to diagnose DR with
more precision. In recent years, algorithms for DR diagnosis have emerged along
with the development of deep learning and the improvement of computer hardware.
However, these usually focus on retina photography. There are no current
methods that can automatically analyze DR using Ultra-Wide OCTA (UW-OCTA). The
Diabetic Retinopathy Analysis Challenge 2022 (DRAC22) provides a standardized
UW-OCTA dataset to train and test the effectiveness of various algorithms on
three tasks: lesions segmentation, quality assessment, and DR grading. In this
paper, we will present our solutions for the three tasks of the DRAC22
challenge. The obtained results are promising and have allowed us to position
ourselves in the TOP 5 of the segmentation task, the TOP 4 of the quality
assessment task, and the TOP 3 of the DR grading task. The code is available at
\url{https://github.com/Mostafa-EHD/Diabetic_Retinopathy_OCTA}.
