Alzheimer's Disease Classification Using Wavelet-Based Image Features

Alzheimer's disease (AD) is a big issue within a population of aged people. AD starts with cognitive decline initially and creates miserable conditions for patients with time. One of the best preventive measures to control AD is its early detection at the Mild Cognitive Impairment (MiCI) i CI) stage. The MiCI i CI is a transition stage between normal ageing and AD. The MiCI i CI stage refers to the noticeable decline in cognitive abilities of a patient, that is more pronounced than would be expected for his age but not severe enough to substantially affect his daily life. Early detection at MiCI i CI stage allows for prompt intervention and medication, which can help manage symptoms more effectively. This paper proposed aAnew feature extraction technique namely, Wavelet-based Shifted Circular-Elliptical Local Descriptors (WSCELD) for early AD detection. The proposed WSCELD combines the Double-Density Dual-Tree Complex Wavelet Transform (DD-DTCWT) with the shifted elliptical and circular local binary patterns for extracting directional and structural features in terms of multiple micro and macro patterns. The histogram features are obtained from transform domain images using the proposed WSCELD and have been used for classification. Different variants of WSCELD viz. Mean WSCELD, Median WSCELD, Energy WSCELD and Variance WSCELD have been investigated and Energy WSCELD has been proposed. Experimental results show the Energy WSCELD as the best performer with classification accuracy, sensitivity, and specificity of 97.3 +/- 1.6%, 97.1 +/- 1.2% and 97.2 +/- 1.1% for AD/Normal Controls (NoC) o C) classification, 94.6 +/- 1.1%, 96.1 +/- 1.2% and 93.1 +/- 1.1% for AD/MiCI i CI classification and 93.8 +/- 1.4%, 92.4 +/- 1.5% and 96.2 +/- 1.2% for MiCI/NoC i CI/N o C classification respectively. The proposed approach is the automated approach for AD detection and is suitable for clinical implementation for early AD detection.