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{175580,
        author = {Tai U. Choramale and Dr. Sharmila M. Shinde},
        title = {Diabetic Retinopathy Detection using Machine Learning (Image Processing): A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3728-3733},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175580},
        abstract = {Diabetic retinopathy (DR) is a serious complication of diabetes mellitus and a leading cause of vision impairment worldwide. Early detection and intervention are crucial in preventing vision loss among diabetic patients. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown remarkable success in various medical image analysis tasks, including the detection of diabetic retinopathy. This paper presents a novel approach for the automated detection of diabetic retinopathy using CNN’s. Our proposed method involves preprocessing retinal fundus images to enhance contrast and remove noise, followed by feature extraction using a pre-trained CNN architecture. The extracted features are then fed into a classification model for the detection of diabetic retinopathy. We utilize a large dataset of annotated retinal images to train and validate our CNN-based detection system, ensuring robust performance across diverse clinical scenarios. Experimental results demonstrate the effectiveness of our approach in accurately detecting diabetic retinopathy, achieving state-of-the-art performance in terms of sensitivity, specificity, and overall accuracy. Moreover, the proposed method exhibits robustness to variations in image quality and pathological characteristics, making it suitable for real-world clinical applications. In conclusion, our study highlights the potential of deep learning, specifically CNNs, as a valuable tool for the early detection and management of diabetic retinopathy. The proposed framework holds promise for integration into existing healthcare systems, facilitating timely diagnosis and intervention to prevent vision loss among diabetic patients.},
        keywords = {Diabetic retinopathy (DR), Convolutional Neural Networks (CNNs), CNN-based Detection System, Deep Learning, etc.},
        month = {April},
        }