Abstract
BackgroundThe detection of Alzheimer’s Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classification of MCI-converts and MCI-non-converts has not been explored profusely and the maximum classification accuracy reported is rather low. Thus, this paper proposes a Machine Learning approach for classifying patients of MCI into two groups one who converted to AD and the others who are not diagnosed with any signs of AD. The proposed algorithm is also used to distinguish MCI patients from controls (CN). This work uses the Structural Magnetic Resonance Imaging data.MethodsThis work proposes a 3-D variant of Local Binary Pattern (LBP), called LBP-20 for extracting features. The method has been compared with 3D-Discrete Wavelet Transform (3D-DWT). Subsequently, a combination of 3D-DWT and LBP-20 has been used for extracting features. The relevant features are selected using the Fisher Discriminant Ratio (FDR) and finally the classification has been carried out using the Support Vector Machine.ResultsThe combination of 3D-DWT with LBP-20 results in a maximum accuracy of 88.77. Similarly, the proposed combination of methods is also applied to distinguish MCI from CN. The proposed method results in the classification accuracy of 90.31 in this data.ConclusionThe proposed combination is able to extract relevant distribution of microstructures from each component, obtained with the use of DWT and thereby improving the classification accuracy. Moreover, the number of features used for classification is significantly less as compared to those obtained by 3D-DWT. The performance of the proposed method is measured in terms of accuracy, specificity and sensitivity and is found superior in comparison to the existing methods. Thus, the proposed method may contribute to effective diagnosis of MCI and may prove advantageous in clinical settings.
Highlights
The detection of Alzheimer’s Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition
The application of 3D-Discrete Wavelet Transform (DWT) results in the maximum accuracy of 0.8574. Based on these observations 3D-Discrete Wavelet Transform (3D-DWT) is combined with Local Binary Pattern (LBP)-20, which results in a maximum accuracy of 0.0.8877
LBP-20 gives a better performance among the three variants of 3D-LBP and is combined with 3D-DWT in the proposed model, 3D-DWT + LBP-20, to extract relevant features from Magnetic Resonance Imaging (MRI) for the classification between i) MCI-C and MCINC and ii) MCI and CN
Summary
The detection of Alzheimer’s Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. This paper proposes a Machine Learning approach for classifying patients of MCI into two groups one who converted to AD and the others who are not diagnosed with any signs of AD. The proposed algorithm is used to distinguish MCI patients from controls (CN). Researchers have identified the following changes in the autopsy studies of people suffering from MCI [6]: Abnormal clusters of beta-amyloid protein (plaques) Microscopic protein clumps of tau characteristic of AD (tangles) Lewy bodies, which are microscopic clumps of another protein associated with forms of dementia like AD and Small strokes or reduced blood flow through brain blood vessels
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