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Wilson’s disease classification using higher-order Gabor tensors and various classifiers on a small and imbalanced brain MRI dataset

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Abstract

Wilson’s Disease (WD) is a rare, autosomal recessive disorder caused by excessive accumulation of Copper (Cu) in various human organs such as the liver, brain, and eyes. Accurate WD diagnosis is challenging because of: (1) subtle intensity variations in infected tissues, and (2) Biased training results in case of a small and imbalanced dataset. This study provides a novel WD classification model for a small MRI dataset (3072 scans). The proposed study explores multi-dimensional Gabor kernels in five scales and eight orientations to produce pixel-specific features and process them in the 4th-order tensor format. The tucker decomposition technique is applied to obtain approximate factors from the Gabor tensors set. Five-fold cross-validation results show that the proposed classification model achieves 99.91% classification accuracy which is better than four well-known feature extraction techniques: (1) 2D-Discrete Wavelet Transform, (2) Intensity histograms, (3) Histogram of oriented gradients, and (4) Grey level co-occurrence matrix. Also, our method improves the classification accuracy by an average of 33% and Area Under the Curve (AUC) by 25% over the above-mentioned feature extraction techniques. In the latter category, the performance of the proposed method is compared with three deep learning models: (1) Customized Convolution Neural Network (CCNN), (2) AlexNet, and (3) VGGNet. In addition, it enhances classification accuracy by 10%, 3.5%, and 3%, compared to CCNN, AlexNet, and VGGNet, respectively. Also, our proposed approach is computationally fast compared to discussed feature extraction techniques.

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  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiyala, 147002, India

    Anurag Tiwari

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Tiwari, A. Wilson’s disease classification using higher-order Gabor tensors and various classifiers on a small and imbalanced brain MRI dataset. Multimed Tools Appl 82, 35121–35147 (2023). https://doi.org/10.1007/s11042-023-14979-w

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