Abstract
The importance of early brain stroke detection cannot be overstated in terms of patient outcomes and mortality rates. Although computed tomography (CT) scan images are frequently used to identify brain strokes, radiologists may not always be accurate in their assessments. Since the advent of deep convolutional neural network (DCNN) models, automated brain stroke detection from CT scan images has advanced significantly. It's probable that current deep convolutional neural network (DCNN) models aren't the best for detecting strokes early on. The authors present a novel deep convolutional neural network model for computed tomography (CT) images-based brain stroke early detection. The ability to extract features, fuse those features, and then recognize strokes is key to the proposed deep convolutional neural network model. To extract high-level information from CT scan images, a feature extractor with numerous convolutional and pooling layers is used.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
