  Medical Imaging is one of the growing fields in the world of computer vision.
In this study, we aim to address the Diabetic Retinopathy (DR) problem as one
of the open challenges in medical imaging. In this research, we propose a new
lesion detection architecture, comprising of two sub-modules, which is an
optimal solution to detect and find not only the type of lesions caused by DR,
their corresponding bounding boxes, and their masks; but also the severity
level of the overall case. Aside from traditional accuracy, we also use two
popular evaluation criteria to evaluate the outputs of our models, which are
intersection over union (IOU) and mean average precision (mAP). We hypothesize
that this new solution enables specialists to detect lesions with high
confidence and estimate the severity of the damage with high accuracy.
