Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sahu, Premananda; Kumar, Ashwani; Jain, Rituraj; Upreti, Kamal; Yadav, Dileep Kumar; Radhakrishnan, G.V. Title A name given to the resource Explainable Hybrid Deep Learning Framework with Multimodal Inputs for Diabetic Retinopathy Detection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived An-Najah University Journal for Research - A (Natural Sciences);Volume;40;Issue;3;pp.319-332 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.35552/anujr.a.40.2.2617" target="_blank" rel="noreferrer noopener">https://doi.org/10.35552/anujr.a.40.2.2617</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038182686?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038182686?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sahu P., School of Computer Science Engineering, Lovely Professional University, Punjab, India; Kumar A., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Yadav D.K., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Radhakrishnan G.V., Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India Description An account of the resource Diabetic Retinopathy (DR) is a leading cause of vision loss, making accurate and interpretable detection critical. This study proposes a hybrid interpretable machinedeep learning framework that integrates multimodal data for enhanced DR severity classification. The model combines unstructured fundus images from EyePACS, Messidor, and APTOS with structured clinical and lifestyle variables such as age, sex, HbA1c, BMI, blood pressure, and diabetes duration. Fundus images undergo preprocessing through resizing, normalization, augmentation, and noise reduction, while clinical data are imputed, normalized, and one-hot encoded. For feature extraction, EfficientNetV2, ResNet50, and Swin Transformer are applied to images, and XGBoost, LightGBM, and TabNet to clinical data. Features are fused via concatenation and attention, followed by classification using Logistic Regression, Random Forest, and MLP. Explainability is provided by Grad-CAM for imaging data and SHAP/LIME for clinical data, supporting clinical interpretability. The proposed model outperformed unimodal baselines, achieving 99.34% accuracy, 98.5% precision, 98.0% recall, 99.0% specificity, 98.2% F1-score, and 0.99 AUC-ROC, with a 10% gain over ResNet50 alone. Performance improvements included a 9% increase in recall and 8% in F1-score, alongside excellent calibration. Confusion matrix analysis confirmed balanced severity detection, and clinicians validated the interpretability outputs. This framework demonstrates robust accuracy, generalization, and clinical applicability for DR screening. 2026, An-Najah National University. All rights reserved. Subject The topic of the resource Diabetic Retinopathy; Explainability; Eyepacs; Fundus Image; Grad-Cam; Lime; Shap Publisher An entity responsible for making the resource available An-Najah National University Relation A related resource ISSN: 17272114; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online