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@article{177963,
        author = {Shaik Masthan and Ponnaganti Madhuri and Chittiboina VenkatSai and Yempuluru Sravika and Kalleti Mahesh},
        title = {Survey on Machine Learning-Driven Early Diagnosis Framework for Brain Stroke Detection using Medical Imaging},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1742-1747},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177963},
        abstract = {Brain stroke, a leading cause of disability and mortality worldwide, occurs due to disrupted blood flow to the brain, requiring rapid diagnosis for effective treatment .Traditional imaging methods like CT and MRI scans play a crucial role in stroke detection, but manual interpretation is time-consuming and prone to variability. With advancements in artificial intelligence, deep learning models, particularly Convolutional Neural Networks (CNNs), have shown remarkable success in medical image analysis. This project leverages a fine-tuned VGG19 model to classify brain stroke images, incorporating data augmentation techniques like rotation, brightness adjustment, and flipping to enhance model generalization. Additionally, preprocessing steps such as image cropping and normalization improve input quality, ensuring robust performance. By splitting the dataset into training, validation, and test sets and evaluating performance through accuracy and loss metrics, this study presents an efficient and automated approach to stroke detection, emphasizing the importance of AI-driven medical imaging solutions.},
        keywords = {Artificial intelligence, Machine learning, Algorithms, pandemic, circular economy (CE), online qualities, resource efficiency, convolution neural networks, computed tomography, m RMR, Oz Net.},
        month = {May},
        }