Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{184874,
        author = {Dr. Sushilkumar N. Holambe and Mr. Pathan AfzalKhan ShadullahKhan},
        title = {Automated Diabetic Retinopathy Detection and classification using ImageNet CNN using Fundus Images: A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {3667-3672},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184874},
        abstract = {Automated detection and grading of diabetic retinopathy (DR) from retinal fundus photographs has rapidly advanced with convolutional neural networks (CNNs), particularly via transfer learning from ImageNet-pretrained models. This review summarizes the evolution of DR detection using ImageNet-based CNNs, common datasets and preprocessing pipelines, architectures and transfer-learning strategies, performance metrics, clinical validation efforts, current limitations (data quality, annotation variability, domain shift, interpretability), and future directions (federated learning, multimodal models, explainability and deployment). Key benchmark results and representative studies are cited to guide researchers aiming to build robust, clinically useful DR screening systems.},
        keywords = {Diabetic retinopathy, fundus imaging, convolutional neural networks, transfer learning, ImageNet, EyePACS, Messidor, grading, screening.},
        month = {October},
        }