Earlier detection of Alzheimer disease using N-fold cross validation approach.

According to the recent study, world-wide 40 million patients are affected by Alzheimer disease (AD) because it is one of the dangerous neurodegenerative disorders. This AD disease has less symptoms such as short term memory loss, mood swings, problem with language understanding and behavioral issues. Due to these low symptoms, AD disease is difficult to recognize in the early stage. So, the automated computer aided system need to be developed for recognizing the AD disease for minimizing the mortality rate. Initially, brain MRI image is collected from patients which are processed by applying different processing steps such as noise removal, segmentation, feature extraction, feature selection and classification. The captured MRI image has noise that is eliminated by applying the Lucy-Richardson approach which examines the each pixel in the image and removes the Gaussian noise which also eliminates the blur image. After eliminating the noise pixel from the image, affected region is segmented by Prolong adaptive exclusive analytical Atlas approach. From the segmented region, different GLCM statistical features are extracted and optimal features subset is selected by applying the hybrid wrapper filtering approach. This selected features are analyzed by N-fold cross validation approach which recognizes the AD related features successfully. Then the efficiency of the system is evaluated with the help of MATLAB based experimental results, in which Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset images are utilized for examining the efficiency in terms of sensitivity, specificity, ROC curve and accuracy.