Diabetic retinopathy causes the retinal blood vessels to deteriorate, resulting in significant eye issues. Automated DR diagnosis frameworks are vital for the early identification and detection of various eye-related diseases, allowing ophthalmic experts to provide a second opinion for effective treatment. Deep learning algorithms have emerged as an upgrade over traditional approaches that rely on handmade feature extraction. DR detection can be classified into four stages: non-retinopathy, mild, moderate, and severe. Fundus image-based DR screening procedures are widely used due to their ease of use, appropriate acquisition, and improved visibility of lesions. The rise in diabetes patients has increased the need for advanced skilled ophthalmologists to initiate the implementation of automatic DR diagnosis systems. The signs of possible DR are not visible to the human eye; thus, a system for automatic early detection of DR is the most important necessity for investigating the characteristics and pattern of DR.