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