Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks

Abstract

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. This is among the most common cause of visual impairment in the working population. Various factors have been discovered to play an important role in a person’s growth of this condition. Among the essential elements at the top of the list are anxiety and long-term diabetes. If not detected early, this illness might result in permanent eyesight loss. The damage can be reduced or avoided if it is recognized ahead of time. Unfortunately, due to the time and arduous nature of the diagnosing process, it is harder to identify the prevalence of this condition. Skilled doctors manually review digital color images to look for damage produced by vascular anomalies, the most common complication of diabetic retinopathy. Even though this procedure is reasonably accurate, it is quite pricey. The delays highlight the necessity for diagnosis to be automated, which will have a considerable positive significant impact on the health sector. The use of AI in diagnosing the disease has yielded promising and dependable findings in recent years, which is the impetus for this publication. This article used ensemble convolutional neural network (ECNN) to diagnose DR and DME automatically, with accurate results of 99 percent. This result was achieved using preprocessing, blood vessel segmentation, feature extraction, and classification. For contrast enhancement, the Harris hawks optimization (HHO) technique is presented. Finally, the experiments were conducted for two kinds of datasets: IDRiR and Messidor for accuracy, precision, recall, F-score, computational time, and error rate.

Research progress in artificial intelligence assisted diabetic retinopathy diagnosis

“…To create a DR detection model, Sundaram et al [42] constructed an AI model using an ensemble convolutional neural network and trained and tested it using three public datasets: DIARETDB0, STARE, and DIARETDB1. Finally, the AI model's accuracy for detecting DR was 0.99, and its detection performance was excellent.…”

“…Sundaram et al [42] Detection diagnosis Fundus image DIARETDB0 dataset, STARE dataset, DIARETDB1 dataset…”

EHSCO-Deep Residual Network: Elephant Herding Sine Cosine Optimization-based Deep Residual network for Diabetic Retinopathy Detection

“…In 2023, Sundaram et.al. [35] have tried Ensemble Convolutional Neural Network (ECNN) for DR detection. Initially, Digital colour was applied to images to differentiate blood vessels and features through Gray-Level Dependence Matrix (GLDM).…”

Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification

“…Non-Proliferative Diabetic Retinopathy (NPDR) leads to retinal swelling and minute blood vessel leaks ( Kadan and Subbian, 2021 ; Nikoloulopoulou et al, 2023 ). The extreme phase in DR is referred to as Proliferative Diabetic Retinopathy (PDR) which damages the inner tissues of the retina and blocks the flow of blood ( Guo and Peng, 2022 ; Li et al, 2022 ; Yan et al, 2022 ; Sundaram et al, 2023 ).…”