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One of the common and escalating endocrine illnesses is diabetes mellitus. Diabetic
retinopathy is a common eye problem in patients with diabetes. Diabetic retinopathy
(DR), a retinal condition, is acknowledged as an epidemic on a global scale. One-third of
the estimated 285 million persons with diabetes show symptoms of DR, and one-third of
them have DR that threatens their vision [1]. In addition, the figures are rising. By 2040,
288 million individuals are expected to have AMD, and by 2050, the number of people
with DR is projected to treble. The need for reliable diabetic retinopathy screening
systems became a critical issue recently due to the increase in the number of diabetic
patients. The severity of DR can be graded into five stages: normal, mild NPDR, moderate
NPDR, severe NPDR, and PDR. Early diagnosis and treatment of DR can be
accomplished by organizing large regular screening programs. Numerous Convolutional
neural network models are developed for the diagnosis of DR in fundus images using
deep learning methods. In Deep Learning (DL) one of the methods is a computer-aided
medical diagnosis for the detection of DR. There are many DL-based methods such as
restricted Boltzmann Machines, convolutional neural networks (CNNs), auto-encoder,
and sparse coding. On the other hand, it is thought-provoking to distinguish it initially not
display signs in the initial classes. The current models for diabetic retinopathy mayn’t
identify entire classes of DR. The utmost commonly used metrics like accuracy, f1-score,
precision, and recall; do not cogitate the standard of difference among labels, which
supports spotting all classes of DR. In our paper used Efficient Net BNs models. We
concluded evaluation scores using the F1-score, which is applicable for grading various
classes of DR established on the intensity levels. We have accomplished the F1- score of
0.88 and 0.84 using the simple preprocess, Gaussian smoothing filters, and deploying an
Efficient Net BNs network on DeepDRiD and EYE-PACS datasets.