This result was achieved using preprocessing, blood vessel segmentation, feature extraction, and classification. This article used ensemble convolutional neural network (ECNN) to diagnose DR and DME automatically, with accurate results of 99 percent. The use of AI in diagnosing the disease has yielded promising and dependable findings in recent years, which is the impetus for this publication. The delays highlight the necessity for diagnosis to be automated, which will have a considerable positive significant impact on the health sector. Even though this procedure is reasonably accurate, it is quite pricey. Skilled doctors manually review digital color images to look for damage produced by vascular anomalies, the most common complication of diabetic retinopathy. Unfortunately, due to the time and arduous nature of the diagnosing process, it is harder to identify the prevalence of this condition. The damage can be reduced or avoided if it is recognized ahead of time. If not detected early, this illness might result in permanent eyesight loss. Among the essential elements at the top of the list are anxiety and long-term diabetes. Various factors have been discovered to play an important role in a person’s growth of this condition. This is among the most common cause of visual impairment in the working population. Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by lesions of varied extent determining the disease burden.
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