ItemConference Paper

Deep learning-based diabetic retinopathy detection with advanced image segmentation and transfer learning techniques

Title
Deep learning-based diabetic retinopathy detection with advanced image segmentation and transfer learning techniques
Creator
Thiyagarajan, Vigneshwaran; Sathiyamurthy, Babu Kumar; Paul, Shourit; Kumar, Prabhat; Channakeshava, Chaitra Palahalli; Shivaram, Shreenidhi Belikothnur
Description
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).
Source
AIP Conference Proceedings;Volume;3345;Issue;1;Article No.;20226;
Date
01-01-2026
Publisher
American Institute of Physics
Subject
Deep Learning; Diabetic Retinopathy; Image Segmentation; Mask RCNN; ReSNet
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 0094243X;
Format
online
Language
English
Type
Conference paper

Citation

Thiyagarajan, Vigneshwaran; Sathiyamurthy, Babu Kumar; Paul, Shourit; Kumar, Prabhat; Channakeshava, Chaitra Palahalli; Shivaram, Shreenidhi Belikothnur, “Deep learning-based diabetic retinopathy detection with advanced image segmentation and transfer learning techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25722.

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