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 Conference Paper Faculty Publications- Conference Papers 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 Thiyagarajan, Vigneshwaran; Sathiyamurthy, Babu Kumar; Paul, Shourit; Kumar, Prabhat; Channakeshava, Chaitra Palahalli; Shivaram, Shreenidhi Belikothnur Title A name given to the resource Deep learning-based diabetic retinopathy detection with advanced image segmentation and transfer learning techniques 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 AIP Conference Proceedings;Volume;3345;Issue;1;Article No.;20226; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0298520" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0298520</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028325217?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028325217?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 Thiyagarajan V., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India; Sathiyamurthy B.K., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India; Paul S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India; Kumar P., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India; Channakeshava C.P., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India; Shivaram S.B., Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Karnataka, Bengaluru, India Description An account of the resource Diabetic retinopathy (DR), a dangerous side effect of diabetes, can result in permanent blindness. This work presents a state-of-the-art deep learning-based system that uses retinal images to detect and classify DR early on. Utilizing transfer learning and pre-trained models, the system combines Django, Numpy, and Keras to improve diagnostic precision. It accurately detects DR-affected areas and delivers real-time graphical outputs for prompt medical interpretation and decision-making using the ResNet and Mask RCNN architectures. Simple picture uploads are made possible by the user-friendly interface, which lets Numpy handle data processing and preparation. To improve accuracy and reduce the amount of new data required, the system uses transfer learning and pre-trained datasets. The system's robustness and efficacy are highlighted by its evaluation, which shows its high accuracy with an overall accuracy of 95.55%, precision, recall, and F1-scores above 0.95. The suggested approach provides an affordable, effective, and scalable means of detecting DR early on; it is especially helpful in healthcare settings with limited resources. The technology has the potential to greatly enhance patient outcomes and lessen the toll that diabetic retinopathy has on both individuals and healthcare systems by enabling prompt diagnosis and treatment. 2026 Author(s). Subject The topic of the resource Deep Learning; Diabetic Retinopathy; Image Segmentation; Mask RCNN; ReSNet Publisher An entity responsible for making the resource available American Institute of Physics Relation A related resource ISSN: 0094243X; Language A language of the resource English Type The nature or genre of the resource Conference paper 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