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@article{174255,
        author = {Dandina Rupa Devi Nagamani and Mokkapati Likhitha Chowdary and Gandham Veera Krishna Chaitanya and Shaik Sameer Bapuji and MR. BELLAMKONDA PURNA CHANDRA SEKHAR},
        title = {Cardiovascular Disease Prediction from Retinal Images Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4125-4131},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174255},
        abstract = {cardiovascular disease (CVD) is recognized as one of the leading causes of mortality worldwide, posing a significant threat to global health. Early detection of CVD is critical for improving patient outcomes, yet traditional diagnostic methods can be invasive, costly, and not always accessible. Interestingly, retinal images offer a non-invasive means of detecting early signs of CVD, as the retina reflects changes in the vascular system that may indicate cardiovascular issues. This project focuses on leveraging retinal imaging in combination with deep learning techniques to predict the presence of CVD. To achieve this, several deep learning models were explored, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), MobileNet, DenseNet, and hybrid models that integrate multiple architectures. The models were trained on a dataset of retinal images labeled according to the presence or absence of CVD. Following training, each model’s performance was evaluated using metrics such as confusion matrix, trainable parameters and accuracy to ensure a comprehensive assessment. This project underscores the potential of deep learning techniques in the early detection of cardiovascular disease through retinal imaging. The promising results open avenues for further research, including the use of larger and more diverse datasets, as well as potential real-world clinical applications to facilitate non-invasive, cost-effective CVD screening.},
        keywords = {cardiovascular disease (CVD), Non-Invasive Diagnosis, Retinal Imaging, MobileNet, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), DenseNet, Hybrid Model.},
        month = {March},
        }