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Two-Dimensional Stockwell Transform and Deep Convolutional Neural Network for Multi-Class Diagnosis of Pathological

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    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |November 25, 2020
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    Summary

    This study introduces a new computer-aided diagnosis (CAD) tool for brain lesion classification using magnetic resonance imaging (MRI). The method efficiently extracts features and uses deep learning for accurate MRI scan diagnosis.

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    Area of Science:

    • Medical Imaging
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Brain lesion detection and classification are critical for medical diagnosis.
    • Machine learning and deep learning have advanced computer-aided diagnosis (CAD) tools.
    • Feature extraction is a key step in machine learning for image classification.

    Purpose of the Study:

    • To investigate brain magnetic resonance imaging (MRI) classification.
    • To develop a robust CAD tool for brain lesion classification.
    • To improve the accuracy of MRI scan diagnosis.

    Main Methods:

    • Utilizing the two-dimensional discrete orthonormal Stockwell transform (2D DOST) for efficient feature extraction from brain MRIs.
    • Applying two-directional two-dimensional principal component analysis ((2D)2PCA) for feature matrix dimension reduction.
    • Designing and training convolution neural networks (CNNs) for MRI classification.

    Main Results:

    • The proposed CAD tool demonstrated superior performance compared to existing methods.
    • The 2D DOST effectively extracted relevant features for classification.
    • The (2D)2PCA successfully reduced feature matrix dimensions.
    • CNNs achieved high accuracy in MRI classification.

    Conclusions:

    • The developed CAD tool, integrating 2D DOST, (2D)2PCA, and CNNs, offers an efficient approach for brain MRI classification.
    • The proposed method significantly enhances the diagnosis of MRI scans.
    • This approach holds promise for improving clinical diagnostic capabilities.