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@article{178078,
        author = {Kajal Shahare and Samiksha Ukey and Janhavi Sontakke and Misbah Shaikh},
        title = {A Comprehensive Survey on Automated Classification of Intracardiac Masses Using Sparse Representation and Deep Learning Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2788-2792},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178078},
        abstract = {Intracardiac masses, which include tumors and thrombi, can critically affect cardiovascular function and patient outcomes. Accurate classification of these masses is pivotal for determining the most effective treatment approaches. This survey reviews advancements in automated classification of intracardiac masses using machine learning (ML) and deep learning (DL) techniques, with a focus on sparse representation, Convolutional Neural Networks (CNNs), and hybrid models. We cover data-related challenges such as sparsity and imbalance, exploring various strategies to address these issues through state-of-the-art techniques. Performance evaluation metrics are analysed, and future directions are suggested to enhance classification accuracy and clinical applicability.},
        keywords = {Convolutional Neural Network (CNN), Deep-Learning, Echocardiography, Feature Extraction, Hybrid Models, Sparse Representation, Transfer Learning,},
        month = {May},
        }