  Much effort is being made by the researchers in order to detect and diagnose
diabetic retinopathy (DR) accurately automatically. The disease is very
dangerous as it can cause blindness suddenly if it is not continuously
screened. Therefore, many computers aided diagnosis (CAD) systems have been
developed to diagnose the various DR grades. Recently, many CAD systems based
on deep learning (DL) methods have been adopted to get deep learning merits in
diagnosing the pathological abnormalities of DR disease. In this paper, we
present a full based-DL CAD system depending on multi-label classification. In
the proposed DL CAD system, we present a customized efficientNet model in order
to diagnose the early and advanced grades of the DR disease. Learning transfer
is very useful in training small datasets. We utilized IDRiD dataset. It is a
multi-label dataset. The experiments manifest that the proposed DL CAD system
is robust, reliable, and deigns promising results in detecting and grading DR.
The proposed system achieved accuracy (ACC) equals 86%, and the Dice similarity
coefficient (DSC) equals 78.45.
