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@article{173873,
        author = {C. Pabitha and G. Santhiya and R. Sharulatha and B.S. Shobika},
        title = {INNOVATIONS IN STROKE IDENTIFICATION: A Deep Learning-Based Diagnostic Model Using Neuroimages},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {1797-1802},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173873},
        abstract = {Considering strokes are one of the top ailments in the world in terms of disability and mortality, accurate and timely diagnosis is critical for successful treatment. With an aim to enhance stroke recognition, this research presents a novel deep learning-based diagnostic model utilizing multi-task heterogeneous ensemble learning and neuroimaging data. The proposed approach integrates multiple machine learning algorithms, each focusing on a specific part of the stroke detection process, including ischemic or hemorrhagic stroke classification, lesion segmentation, and severity level estimation. By combining the statistical approach, deep learning, and traditional machine learning, the ensemble model guarantees reliable and accurate diagnosis of strokes. Extensive experiments conducted on publicly available neuro imaging databases verified the model's ability to improve the accuracy, sensitivity, and specificity of the diagnosis. The study aims to improve Deep learning-based medical diagnosis by offering an interpretable and scalable solution for prompt stroke detection.},
        keywords = {deep learning algorithms, YOLO, CNN, ANN},
        month = {March},
        }