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@article{171980,
        author = {Sakshi B Raj and Sadhvi G N and Sanidhya H R},
        title = {Comprehensive survey on deep learning approach for prediction of heart attack using retinal eye images.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {1506-1510},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171980},
        abstract = {The study on retinal fundus eye images explores the application of deep learn ing techniques in analyzing the early detection of heart attacks. By examining the unique characteristics of retinal vasculature, it helps identify vascular abnormalities that may indicate underlying cardiovascular risks, specifically heart attacks. Cardiovascular diseases (CVDs), including heart attacks, are among the leading causes of premature death worldwide, making early detection crucial for timely intervention and prevention. The proposed models emphasize identifying and extracting features from segmented retinal fundus images, focusing on key vascular patterns that reflect potential cardiovascular risks. These extracted features are processed and analyzed using Convolutional Neural Networks (CNNs) built on EfficientNet-B0 and VGG16 architectures to enhance accuracy and efficiency. The standout features of this study include pre-processing techniques like Intensity Scaling and other methods, feature extraction from segmented images, classification using advanced CNN architectures, and comparing the model’s performance with existing methods to demonstrate significant improvements in overall accuracy. This approach offers promising accuracy in heart attack prediction using retinal fundus images, leveraging deep learning for more reliable and effective early detection.},
        keywords = {Early Detection, Deep Learning, Convolutional Neural Networks (CNN), Cardiovascular diseases, Efficient Net b0, VGG16, Vascular Abnormalities, Image Classification.},
        month = {January},
        }