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@article{195336,
        author = {KONETI SAIRAM AVINASH and Manchala Sai Srilekha and Kona Rajesh and Pothamsetty Girish Babu and P. Sathyanarayana},
        title = {EffNet-SVM: A Hybrid Model for Diabetic Retinopathy Detection Using Retinal Fundus Images},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {106-108},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195336},
        abstract = {Diabetic Retinopathy (DR) is a severe complication of diabetes and one of the leading causes of blindness worldwide. Early detection is critical to prevent vision loss, but manual diagnosis is time-consuming and dependent on expert availability. This paper proposes a hybrid model named EffNet-SVM for automated detection of diabetic retinopathy using retinal fundus images. The system integrates EfficientNetV2-S for feature extraction and Support Vector Machine (SVM) for classification. EfficientNet extracts high-level visual features, while SVM ensures robust and accurate classification using an RBF kernel. To improve interpretability, the model incorporates explainable AI techniques such as Grad-CAM and LIME. The system is deployed using Streamlit, enabling real-time prediction and visualization. Experimental results show that the proposed approach achieves high accuracy with reduced computational complexity, making it suitable for real-world healthcare applications.},
        keywords = {Diabetic Retinopathy, EfficientNet, SVM, Deep Learning, Explainable AI, Medical Imaging},
        month = {April},
        }