An Optimized CaraNet–Reflection-Equivariant Quantum Neural Framework with Gradient Domain-Guided Filtering and Emperor Penguin Optimization for Diabetic Retinopathy Detection

Diabetic retinopathy (DR) is one of the major causes of vision impairment in diabetic patients in the contemporary world. Recently, DR, an aberrant disorder connected to the human retina, has gained international attention. Adults are more likely to have DR, which can result in both minor and significant blindness, as a result of modern humans’ increased daily screen-related activities. Because there are so many patients, doctors and clinicians are unable to make early diagnoses. To address this issue, this research integrates gradient domain-guided filtering for image preprocessing, which guarantees improved noise reduction and edge preservation, and proposes an updated framework for DR classification and detection. The enhanced Mask Region-Based Convolutional Neural Network (Mask R-CNN) is used to get the results in accurate segmentation. Special Reflection Equivariant Quantum Neural Networks (REQNNs) are matched with the Context Axial Reverse Attention Network (CaraNet) to quickly distinguish the features. Optimization is done under the Emperador Penguin Optimizer (EPO), which is known to converge very fast and provide top performance. To assess the effectiveness of the suggested approach, key measures like as area under the curve (AUC), F1-score, recall, accuracy, and precision were calculated using the Messidor-2, APTOS 2019, and EyePACS datasets. The proposed method outperformed a number of cutting-edge baselines, including AdaBoost, TL-CNN, and DenseNet-121, by achieving remarkable results with metrics above 99% on all datasets. These results demonstrate not only the framework’s dependability, stability, and efficacy in detecting DR, but also its potential for practical use in mass screening programs and lowering physician workload. However, the framework’s training demands big, well-annotated datasets, which may limit its usefulness in environments with limited resources. All things considered, the study presents a reliable and precise method for DR classification and detection that can support early intervention and enhance patient outcomes.