Wavelet Transform based Alzheimer’s Disease detection using Grey Level Co-occurrence Matrix and Genetic Algorithm

Abstract

Alzheimer’s disease (AD) is a neurological, irreversible disorder of the brain that reduces the cognitive skills and memory ability. Alzheimer’s Disease should be diagnosed in initial phase for providing efficacious treatment to the patient. Delay in treatment may lead to the demise of patient. This paper presents a new technique for feature extraction in Alzheimer’s Disease classification. Dual Tree complex wavelet transform is applied to decompose the image into six sub images. Subsequently, Gray Level Co-occurrence Matrix is calculated from six sub images. From these matrices 22 statistical features are obtained for each sub-band image. Further, Genetic Algorithm is used to obtain the relevant and optimum features to improve the classification accuracy. Dual Tree Complex Wavelet Transform shows high directional selectivity, so it can decompose the image from six different directions and provide additional informative features. In addition, Gray Level Co¬occurrence Matrix deals with the image intensity directly, so it provides the information related with the patterns which are mutually occurring. Genetic Algorithm requires less information and provides more solutions, so it extracts optimum features and improves classification accuracy. The proposed method obtained 96.2% accuracy, 96.4% sensitivity and 95.9% specificity. The performance of the proposed work surpasses the existing traditional approaches for AD detection.