Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetics. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today’s growing medical field presents a more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model’s capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image is preprocessed using contrast-limited adaptive histogram equalization and the discrete wavelet transform. The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy and the Asia-Pacific Tele-Ophthalmology Society. The CNN model is tuned in with different learning rates and optimizers. An accuracy of 72% and an area under curve score of 0.90 was achieved by the CNN model with the Adam optimizer. The recommended study results may reduce diabetes-related vision impairment by early identification of DR severity.

Background: Diagnostic errors can be substantially diminished, and clinical decision-making can be significantly enhanced through automated image classification. Methods: We implemented a YOLO (You Only Look Once)-based system to classify diabetic retinopathy (DR) utilizing a unique retinal dataset. Although YOLO provides exceptional accuracy and rapidity in object recognition and categorization, its interpretability is constrained. Both binary and multi-class classification methods (graded severity levels) were employed. The Contrast-Limited Adaptive Histogram Equalization (CLAHE) model was utilized to improve image brightness and detailed readability. To improve interpretability, we utilized Eigen Class Activation Mapping (Eigen-CAM) to display areas affecting classification predictions. Results: Our model exhibited robust and consistent performance on the datasets for binary and 5-class tasks. The YOLO 11l model obtained a binary classification accuracy of 97.02% and an Area Under Curve (AUC) score of 0.98. The YOLO 8x model showed superior performance in 5-class classification, with an accuracy of 80.12% and an AUC score of 0.88. A simple interface was created using Gradio to enable real-time interaction. Conclusions: The suggested technique integrates robust prediction accuracy with visual interpretability, rendering it a potential instrument for DR screening in clinical environments.

Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetic people. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today's growing medical field poses more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model's capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image undergoes preprocessing using contrast-limited adaptive histogram equalization (CLAHE) and the discrete wavelet transform (DWT). The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy (DDR) and the Asia Pacific Tele-Ophthalmology Society (APTOS). The CNN model is tuned with different learning rates and optimizers. The CNN model with Adam optimizer achieved 70% accuracy and an area under curve (AUC) score of 0.82. The recommended study results may reduce diabetes-related vision impairment by early DR severity identification.

Diabetic Macular Edema (DME) poses a significant threat to vision, often leading to permanent blindness if not detected and addressed swiftly. Existing manual diagnostic methods are arduous and inconsistent, highlighting the pressing necessity for automated, accurate, and personalized solutions. This study presents a novel methodology for diagnosing DME and categorizing choroidal neovascularization (CNV), drusen, and normal conditions from fundus images through the application of transfer learning models and bio-inspired optimization methodologies. The methodology utilizes advanced transfer learning architectures, including VGG16, VGG19, ResNet50, EfficientNetB7, EfficientNetV2-S, InceptionV3, and InceptionResNetV2, for analyzing both binary and multi-class Optical Coherence Tomography (OCT) datasets. We combined the OCT datasets OCT2017 and OCTC8 to create a new dataset for our study. The parameters, including learning rate, batch size, and dropout layer of the fully connected network, are further adjusted using the bio-inspired Particle Swarm Optimization (PSO) method, in conjunction with thorough preprocessing. Explainable AI approaches, especially Shapley additive explanations (SHAP), provide transparent insights into the model’s decision-making processes. Experimental findings demonstrate that our bio-inspired optimized transfer learning Inception V3 significantly surpasses conventional deep learning techniques for DME classification, as evidenced by enhanced metrics including the accuracy, precision, recall, F1-score, misclassification rate, Matthew’s correlation coefficient, intersection over union, and kappa coefficient for both binary and multi-class scenarios. The accuracy achieved is approximately 98% in binary classification and roughly 90% in multi-class classification with the Inception V3 model. The integration of contemporary transfer learning architectures with nature-inspired PSO enhances diagnostic precision to approximately 95% in multi-class classification, while also improving interpretability and reliability, which are crucial for clinical implementation. This research promotes the advancement of more precise, personalized, and timely diagnostic and therapeutic strategies for Diabetic Macular Edema, aiming to avert vision loss and improve patient outcomes.

The precise and prompt identification of ocular conditions, such as diabetic retinopathy (DR), diabetic macular edema (DME), or glaucoma, often depends on the collaborative analysis of medical imaging techniques such as fundus photography or optical coherence tomography (OCT), alongside the patient’s demographic and acquisition metadata. This research introduced a unique unimodal and multimodal neural network (multimodal net) architecture that leverages complementary information for disease categorization. The unimodal approach has an individual architecture (an image branch and a tabular branch), whereas the multimodal net has a dual-branch architecture (an image branch that leverages a pre-trained ResNet-18 to extract deep visual features from OCT images and a tabular branch that includes a multi-layer perceptron [MLP] to analyze demographic and acquisition metadata factors). The features obtained from both modalities are concatenated and then integrated through a combined branch to predict the target illness class. By combining these diverse data types, the model aimed to gain a holistic understanding of the patient’s condition, potentially yielding a more robust and demographic-relevant diagnostic system than those that rely on a single modality. The trial findings showed that the multimodal method, which combines patient demographics and image details with retinal OCT images, performs slightly better than unimodal approaches. With 94.92% accuracy and an Misclassification Rate (MCC) of 0.0508, the EfficientNet-B0 + MLP configuration significantly outperformed the ResNet18 baseline (86.9% accuracy) for the image-level split and achieved 0.924 validation accuracy with 0.871 MCC for the patient-level split. Further, we validated the fusion layer’s ability to capture synergies between visual and tabular data through five-fold cross-validation, which improved performance to an overall accuracy with a mean and standard deviation of 0.932 ± 0.006 and macro-F1 of 0.876 ± 0.023 for patient-level splits. Moreover, we compared our proposed model with image-only, tabular-only, and gated multimodal fusion (GMU)-fused structures. The image-only model showed strong predictive performance (91.23% accuracy, 0.94 Area Under the Curve (AUC)) compared with the tabular-only model (64.66% accuracy), although integrating both modalities yielded even better outcomes. The suggested concat (image + tabular) framework performed best, achieving 92.43% accuracy (95% CI: 0.895–0.947), a macro-AUC of 0.9703, and a minimal misclassification rate of 0.77, illustrating the benefits of combining demographic and acquisition tabular attributes with visual information. We adopted EfficientNet-B0 as our primary encoder. To assess whether performance depends on this choice, we also evaluated ResNet-18, ViT, Swin, MaxViT, and CoAtNet. All encoders achieved comparable macro-F1 (0.86–0.89), with differences within the range of single-split variation, confirming that the results are not specific to a single architecture. Grad-CAM (Gradient-weighted Class Activation Mapping) visualizations were also reported.