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@article{175016,
        author = {Vasantha Laxmi Jetti and Prameela Boddepalli and Venkata Srikara Praneeth Kanagala and Sowmya Rani Madimi and Jashwanth Chintala and Divakar Naidu Laveti},
        title = {Multi-Stage Image Processing based Ocular recognition using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1696-1704},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175016},
        abstract = {Early detection and classification of ocular diseases are crucial for preventing vision impairment and enabling timely medical intervention. This study leverages deep learning techniques to classify ocular diseases using the ODIR-5K fundus image dataset [3]. To address class imbalance, we explored Variational Autoencoders (VAEs) and Deep Convolutional Generative Adversarial Networks (DCGANs), with DCGANs successfully generating high-quality synthetic images for underrepresented classes [4]. Our preprocessing pipeline includes Contrast Limited Adaptive Histogram Equalization (CLAHE) and Gamma correction for contrast enhancement [2], followed by data augmentation using RGB channel splitting to improve feature extraction [5]. We fine-tuned ResNet-50 and ResNet-101 using transfer learning [6], achieving classification accuracies of 93.67% and 94.3%, with Kappa scores of 0.8976 and 0.9123, respectively. These results highlight the efficacy of deep learning in ocular disease classification, demonstrating potential for real-world clinical applications [8, 11].},
        keywords = {Ocular Disease Classification, Deep Learning, Generative Adversarial Networks, Transfer Learning, Fundus Image Analysis, Medical Image Processing, Automated Diagnosis.},
        month = {April},
        }