OPU-NET-DADENA: Optimized deep learning ensemble with u-net segmentation for early detection of diabetic retinopathy

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: 99.33% accuracy, 98.32% precision

• Primary Outcome: Diagnostic accuracy for early diabetic retinopathy detection

• Nnt Or Sensitivity: Sensitivity not reported - critical limitation for screening test

• Confidence Interval: Not reported

Clinical Application

Requires MATLAB implementation and specialized technical expertise, limiting clinical deployment Could potentially support RACGP diabetes management guidelines for annual retinal screening, but requires validation against Australian population and integration with existing teleophthalmology programs Patients with diabetes mellitus requiring retinopathy screening

Abstract

One of the most significant microvascular complications of diabetes mellitus (DM) is diabetic retinopathy (DR). In the early stages, patients with DR may not exhibit any noticeable symptoms. It is a diabetic disorder that damages retinal blood vessels in the eyes. At first, there are no symptoms or sporadic visual issues. When it worsens, it affects both eyes and can lead to partial or total blindness. A person who already has DM is therefore constantly at a higher risk of developing the condition. Early identification can prevent the possibility of total and irreversible blindness. Therefore, needs an efficient and early diagnosis system. So, this paper proposes a new deep-learning methodology in a specific Deep Siamese DenseNet for early detection of DR. The proposed method is accomplished through various steps, such as Data Collection, Preprocessing, Augmentation, Segmentation, Feature Extraction, Feature Selection, and Detection. An adaptive histogram equalization approach named Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to preprocess input images, which reduces amplification of noise. Then, the segmentation is done by the Optimized U-NETs. Next, features of the contrasted retinal images are extracted by residual attention EfficientNet (RA-EfficientNet). Then, the optimal features are selected by a hybrid Reptile Search Algorithm (RORS). Finally, the deep learning methodology includes DarkNet, DenseNet 201, and NasNetMobile used to detect diabetic retinopathy at an early stage. The model is implemented in MATLAB and evaluated using accuracy, precision, F-score, specificity, sensitivity, MCC, NPV, FPR, and FNR. The proposed approach achieves 99.33% accuracy and 98.32% precision, outperforming previous methods that reported accuracies of 95-97% and precisions of 94-96%, demonstrating its effectiveness for reliable early detection of DR.