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    Brain stroke detection from computed tomography images using deep learning algorithms

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    Author
    Diker, Aykut
    Elen, Abdullah
    Subasi, Abdulhamit cc
    Subject
    Stroke Classification
    Residual Convolutional Neural Network
    VGG-19
    AlexNet
    GoogleNet
    Date
    2023-01-01
    
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    Abstract
    Stroke is one of the common causes of death worldwide. Stroke is the inability of a focus to be fed in the brain due to clogged or bleeding of the vessels feeding the brain. Because early stroke treatment and diagnosis are related to a favorable patient outcome, time is a critical aspect of successful stroke treatment. In this chapter, we examine the stroke classification from Brain Stroke CT Dataset, with deep learning architectures. In the experimental study, a total of 2501 brain stroke computed tomography (CT) images were used for testing and training. For this purpose, numerus widely known pretrained convolutional neural networks (CNNs) such as GoogleNet, AlexNet, VGG-16, VGG-19, and Residual CNN were used to classify brain stroke CT images as normal and as stroke. Several performance metrics such as accuracy (ACC), specificity (SPE), sensitivity (SEN), and F-score are used to evaluate the performances of the classifier. The best classification results are achieved by VGG-19 with ACC 97.06%, SEN 97.41%, SPE 96.49%, and F-score 96.95%.
    Department
    Computer Science
    Publisher
    Academic Press
    Book title
    Applications of Artificial Intelligence in Medical Imaging.
    DOI
    https://doi.org/10.1016/B978-0-443-18450-5.00013-X
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1016/B978-0-443-18450-5.00013-X
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