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    A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet

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    bioengineering-09-00783-v2.pdf
    Embargo:
    2023-12-08
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    1.876Mb
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    Author
    Ozaltin, Oznur
    Coskun, Orhan
    Yeniay, Ozgur
    Subasi, Abdulhamit cc
    Subject
    Brain Stroke
    Classification
    Convolutional Neural Networks (CNNs)
    Computed Tomography
    Feature Extraction
    mRMR
    OzNet
    Date
    2022-12-01
    
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    Abstract
    A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. After the stroke, the damaged area of the brain will not operate normally. As a result, early detection is crucial for more effective therapy. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. However, while doctors are analyzing each brain CT image, time is running fast. This circumstance may lead to result in a delay in treatment and making errors. Therefore, we targeted the utilization of an efficient artificial intelligence algorithm in stroke detection. In this paper, we designed hybrid algorithms that include a new convolution neural networks (CNN) architecture called OzNet and various machine learning algorithms for binary classification of real brain stroke CT images. When we classified the dataset with OzNet, we acquired successful performance. However, for this target, we combined it with a minimum Redundancy Maximum Relevance (mRMR) method and Decision Tree (DT), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), and Support Vector Machines (SVM). In addition, 4096 significant features were obtained from the fully connected layer of OzNet, and we reduced the dimension of features from 4096 to 250 using the mRMR method. Finally, we utilized these machine learning algorithms to classify important features. As a result, OzNet-mRMR-NB was an excellent hybrid algorithm and achieved an accuracy of 98.42% and AUC of 0.99 to detect stroke from brain CT images.
    Department
    Computer Science
    Publisher
    Multidisciplinary Digital Publishing Institute
    Journal title
    Bioengineering
    DOI
    https://doi.org/10.3390/bioengineering9120783
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.3390/bioengineering9120783
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