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@article{171588,
        author = {Bhavya.B and Nagaraja S R and Eranti Sai Kishan and Eranti Sai Dinesh and Lakki Reddi Varshitha and Meghana GS},
        title = {Advanced Image Processing Techniques for Medical Diagnostics in Brain Stroke Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {717-720},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171588},
        abstract = {Stroke is the main cause of disability and death worldwide. There is a significant need for the development of early and accurate mechanisms for stroke prediction. The following project is a new innovative approach to the brain stroke prediction and classification process based on advanced deep learning models, such as 2D-CNN, VGG Model. These models have been optimized for the purposes of the precise segmentation of medical images such as CT and MRI scans.
The proposed system would integrate a user-friendly web interface that allows for the easy input of CSV data or medical images, followed by automated processing using state-of-the-art AI models. By combining traditional machine learning methods with cutting-edge deep learning techniques, the solution would enhance predictive accuracy, automate segmentation, and reduce diagnostic time.
This is an interdisciplinary framework that seeks to provide an efficient, scalable, and robust tool for the early detection of stroke. This work is integrated into clinical workflows and seeks to help healthcare professionals improve patient outcomes through timely intervention.},
        keywords = {cerebrovascular accident (CVA), convolutional neural network(CNN), Brain stroke prediction, deep learning, image segmentation, medical imaging, AI in healthcare, 2D-CNN models.},
        month = {January},
        }