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    Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

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    Author
    Ozaltin, Oznur
    Coskun, Orhan
    Yeniay, Ozgur
    Subasi, Abdulhamit cc
    Subject
    Neighborhood Component Analysis (NCA)
    Artificial Intelligence (AI)
    Convolutional Neural Networks (CNN)
    K-Nearest Neighbor (K-NN)
    Linear Discriminant Analysis (LDA)
    Support Vector Machines (SVM)
    Naïve Bayes
    Date
    2023-01-12
    
    Metadata
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    Abstract
    Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.
    Department
    Computer Science
    Publisher
    John Wiley & Sons, Inc.
    Journal title
    International Journal of Imaging Systems and Technology
    DOI
    https://doi.org/10.1002/ima.22806
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1002/ima.22806
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